Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction

Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction Mingxing Yan, Liping Hu, Mengting Chen, Jun Shi, Feng Li, jinji Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4892379/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to identify antenatal and intrapartum risk factors associated with cesarean delivery in term singleton pregnancies complicated by small for gestational age (SGA) and to develop a predictive model. Methods: We conducted a retrospective case-control study of 507 SGA patients who underwent labor induction between 2017 and 2022 at Fujian Maternity and Child Health Hospital.Comprehensive data on maternal demographics, obstetric complications, labor induction methods, and neonatal outcomes were collected. 354 (70%) experiencing SGA complications enrolled as the derivation cohort and 153 (30%) included in the validation set. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for cesarean delivery, and a predictive nomogram was developed based on these factors in the derivation cohort,and verified in the validation set. Results: A total of 134 (26.43%) women in the cohort underwent cesarean delivery following labor induction. Four significant independent risk factors for cesarean delivery were identified: maternal age(aOR1.08, 95%CI 1.01-1.15) , weightat admission (aOR 1.04, 95% CI 1.01 - 1.07), the use of dinoprostone for induction(aOR 2.08, 95% CI 1.13-3.81), and the Bishop score after cervical ripening(aOR0.65, 95% CI:0.54-0.80). The constructed nomogram displayed a discriminative ability with an area under the curve (AUC) of 0.78 in the training cohort and 0.77 in the validation cohort. Calibration curves indicated strong agreement（P＞0.05）between predicted probabilities and observed outcomes, while decision curve analysis confirmed significant net benefits across various various threshold probabilities. Conclusion: The developed nomogram provides clinicians with a reliable tool for predicting the likelihood of cesarean delivery in SGA pregnancies undergoing labor induction, aiding in informed decision-making and potentially optimizing clinical management strategies to improve perinatal outcomes. Small for Gestational Age (SGA) Cesarean Delivery Labor Induction Risk Factors Predictive Model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Small for gestational age (SGA) refers to infants whose birth weight is below the 10th percentile for their same gestational age and sex [ 1 ].As a high-risk group among newborns, SGA infants are predisposed to various perinatal diseases and mortality. They are also susceptible to multiple long-term metabolic and cardiovascular conditions. Moreover, they face significantly increased risks of delayed growth, developmental delays, and neurological disorders, profoundly affecting their quality of life in long- and short-term[ 2 – 3 ]. Due to the heightened risk of stillbirth in pregnancies beyond 37 weeks of gestation, there is a common consensus advocating for labor induction during the early term phase [ 4 ]. However, the incidence of cesarean delivery remains notably high in this context, primarily due to the associated risks of intrapartum fetal acidosis and fetal heart rate (FHR) abnormalities commonly seen in SGA fetuses[ 5 ]. Previous studies have identified several risk factors contributing to cesarean delivery[6–9], including Doppler abnormalities in the umbilical artery, a reduced cerebroplacental ratio (CPR), oligohydramnios, the utilization of prostaglandins, and non-reassuring fetal heart tracing(NRFHT). Determining the appropriate timing and method of delivery for fetuses with SGA poses considerable challenges across all stages of gestational age. Thus, it is vital to identify antenatal and intrapartum risk factors associated with cesarean delivery at labor induction. The objective of this research is to develop and validate an objective model for predicting cesarean deliveries due to failed labor inductions in pregnancies complicated by the SGA in term singletons.This model seeks to enhance informed decision-making, optimize resource allocation, and mitigate the potential risks associated with failed labor induction. Materials and Methods Study Population A retrospective case-control study was carried out at Fujian Maternity and Child Health Hospital, involving a series of pregnant women diagnosed as having small for gestational age fetuses between October 2017 and December 2022. Ethical approval was obtained for the study from the hospital’s Ethics Committee (reference number 2023KY016). Comprehensive maternal and neonatal characteristics were systematically collected from medical records. Considering the study’s retrospectinve appoach and the anonymous date of the patients, obtaining informed consent was deemed unnecessary. The initial inclusion criteria included singleton pregnancies diagnosed with SGA, characterized by a birth weight that falls under the 10th percentile at term (≥ 37 weeks). Exclusion criteria comprised multiple pregnancies, congenital malformations, fetal chromosomal abnormalities, premature rupture of membranes (PROM), prior uterine surgery (such as cesarean section or myomectomy), and cases lost to follow-up. In total, 507 cases were evaluated for labor induction, all presenting cephalically with a Bishop score < 6 for cervical ripening. Figure 1 depicts the selection procedure. Treatment Protocol for SGA Pregnancies and Induction Methods In accordance with departmental protocols, all SGA patients underwent routine ultrasound and fetal heart rate monitoring prior to labor induction. A senior physician assessed the cervical condition via the Bishop score. Patients were thoroughly informed about the induction procedure and its potential risks, including those related to the use of Cook's double balloon and dinoprostone. Each SGA patient provided informed consent, selecting their preferred method for cervical ripening. Following manufacturer guidelines, 10mg of dinoprostone was introduced into the posterior vaginal fornix, while 80ml of saline was used to inflate both intrauterine and intravaginal balloons for the Cook's double balloon. In our labor induction protocol, each method was applied for a maximum of 12 hours, accompanied by intermittent cardiotocographic monitoring. Criteria for removal included reaching the active phase of labor (≥ 2cm cervical dilation), membrane rupture, non-reassuring fetal heart rate, placental abruption, or uterine tachysystole (> 5 contractions in 10 minutes) for either device. If active labor (≥ 2cm cervical dilation) was not achieved within 12 hours, the devices were removed, and intravenous oxytocin was initiated at a rate of 2.5 mIU/min, with adjusted every 15 minutes based on uterine contractions. Women achieving vaginal delivery following induction were classified as successful, whereas those requiring cesarean delivery for any reason were considered failed inductions. Data Collection The primary outcome was to identify risk factors linked to cesarean birth in pregnancies complicated by SGA during labor induction. The analyzed factors included maternal demographic characteristics such as age, height, weight, body mass index (BMI), gravidity, and parity;complications during pregnancy like gestational diabetes, pregnancy-induced hypertension and oligohydramnios, obstetric conditions including gestational age, Bishop scores before and after cervical ripening, induction methods, oxytocin augmentation, and mode of delivery, complications during labor such as fever, early rupture of membranes, and blood loss after birth, and neonatal characteristics like estimated fetal weight, neonatal weight, Apgar scores at 1 and 5 minutes, and admission to the neonatal intensive care unit (NICU)). Statistical Analysis Numerical variables were presented as mean ± standard deviation or median (interquartile range), whereas categorical variables were expressed as frequencies and percentages.The Student’s t-test or Mann–Whitney U-test was employed for comparing continuous variables, while the chi-square (χ²) test or Fisher’s exact test was utilized for categorical variables.Risk factors correlated with cesarean delivery were identified through multivariate binary logistic regression analysis, applying univariate predictors (p < 0.05) via a stepwise backward elimination method. In the multivariate analysis,only variables that had a p-value of less than 0.05 in the univariate analysis were included,whereas insignificant factors were omitted. Instances of multicollinearity necessitated the retention of only one variable within the model.All statistical tests were performed as two-tailed, considering p-values < 0.05 as statistically significant. Associations were expressed as odds ratios (OR),accompanied by 95% confidence intervals (CIs). A graphical nomogram was generated to visually represent the logistic regression model. The model’s discriminatory performance was quantified by the area under the receiver operating characteristic (ROC) curve (AUC). The ROC curve was derived from the regression model, with both the AUC and its 95% CIs reported. AUC values exceeding 0.7 suggest robust discriminatory capacity. Calibration was evaluated using the Hosmer-Lemeshow test, accompanied by a calibration curve illustrating predicted versus actual probabilities, with alignment closely following the ideal 45° line indicating strong correlation. Clinical applicability was assessed via decision curve analysis (DCA). All statistical analyses were executed using R (version 4.3.0; The R Project for Statistical Computing, Vienna, Austria; http://www.r-project.org ) and SPSS (version 24.0; IBM Corp., Armonk, NY, USA). Results Baseline Patient Characteristics The participant selection flowchart for our case-control study is illustrated in Figure 1. Of 998 patients meeting inclusion criteria, 491 were excluded due to factors such as multiple pregnancies, chromosomal abnormalities, and major malformations. Ultimately, a cohort of 507 participants was included in this study, with 354 (70%) experiencing SGA complications enrolled as the derivation cohort and 153 (30%) included in the validation set. A total of 134 (26.43%) women in the cohort underwent cesarean delivery following labor induction. As depicted in Table 1,a comparison was made between the maternal, neonatal, and obstetric characteristics of women who achieved vaginal delivery and those who required cesarean section. Women who underwent cesarean section demonstrated higher maternal age (29.68 ± 4.58 vs. 28.19 ± 4.22, P < 0.001), greater admission weight (62.81 ± 8.39 vs. 60.65 ± 9.57, P = 0.015), and elevated admission BMI (24.84 ± 2.93 vs. 24.00 ± 3.66, P = 0.008) in comparison to vaginal delivery patients. Cesarean deliveries were also associated with lower Bishop scores following cervical ripening (6.17 ± 1.41 vs. 7.05 ± 1.60, P < 0.001), An increased incidence of oligohydramnios (16.42% vs. 9.92%, P = 0.044), lower rates of early rupture of membranes during induction (5.97% vs. 12.87%, P = 0.029), and increased postpartum blood loss (397.5 ml vs. 185 ml, P < 0.001). Notably, the utilization of dinoprostone for induction resulted in a statistically significant cesarean delivery rate of 47.01% among SGA patients when compared to the Cook double balloon method. Table 2 provides the characteristics of pregnancies in both the derivation and validation cohorts, which shared similar maternal and fetal features, indicating the robustness of random group assignment. Table 1 Univariate analysis of demographic and clinical characteristics of pregnancies with SGA stratified by mode of delivery (N=507) Variables Total (N= 507) vaginal delivery (N= 373) cesarean delivery (N = 134) P Antenatal variables Maternal age(y) 28.59 ± 4.36 28.19 ± 4.22 29.68 ± 4.58 <.001 * Maternal age, n(%) 0.006 * ＜35 458 (90.34) 345 (92.49) 113 (84.33) ≥35 49 (9.66) 28 (7.51) 21 (15.67) Gravidity 1.62 ± 0.95 1.62 ± 0.92 1.63 ± 1.02 0.915 Gestational age at adimission 38.99 ± 1.19 39.00 ± 1.19 38.96 ± 1.20 0.707 Height（cm） 158.95 ± 5.16 158.95 ± 5.05 158.95 ± 5.47 0.993 Prepregnancy weight（kg) 50.44 ± 7.34 50.17 ± 7.32 51.17 ± 7.35 0.178 Prepregnancy BMI(kg/m2) 19.95 ± 2.68 19.85 ± 2.76 20.22 ± 2.42 0.174 Prepregnancy BMI，n(%) 0.314 Underweight(BMI < 18.5) 154 (30.37) 121 (32.44) 33 (24.63) Normal(18.5 ≤ BMI < 25.0) 312 (61.54) 222 (59.52) 90 (67.16) Overweigt(25.0 ≤BMI < 30.0) 38 (7.50) 28 (7.51) 10 (7.46) Obese (BMI ≥ 30.0) 3 (0.59) 2 (0.54) 1 (0.75) Admission weight(kg) 61.22 ± 9.31 60.65 ± 9.57 62.81 ± 8.39 0.015 * Admission BMI(kg/m2) 24.23 ± 3.50 24.00 ± 3.66 24.84 ± 2.93 0.008 * Admission BMI (%) 0.003 * Underweight(BMI < 18.5) 27 (5.33) 25 (6.70) 2 (1.49) Normal(18.5 ≤ BMI < 25.0) 210 (41.42) 165 (44.24) 45 (33.58) Overweight(25.0 ≤ BMI < 30) 205 (40.43) 135 (36.19) 70 (52.24) Obese (BMI ≥ 30.0) 65 (12.82) 48 (12.87) 17 (12.69) Parity , n(%) 0.263 0 383 (75.54) 277 (74.26) 106 (79.10) ≥1 124 (24.46) 96 (25.74) 28 (20.90) GDM, n(%) 74 (14.60) 58 (15.55) 16 (11.94) 0.31 Gestational hypertension, n(%) 34 (6.71) 27 (7.24) 7 (5.22) 0.424 Oligoamnios, n(%) 59 (11.64) 37 (9.92) 22 (16.42) 0.044 * Bishop score before labor induction 2.85 ± 0.86 2.88 ± 0.86 2.76 ± 0.83 0.181 Estimated fetal weight(kg) 2650.20 ± 266.55 2655.31 ± 249.88 2635.96± 308.83 0.515 Intrapartum variables Method of inducing labor, n(%) 0.007 * cook double balloon 318 (62.72) 247 (66.22) 71 (52.99) dinoprostone 189 (37.28) 126 (33.78) 63 (47.01) Bishop score after medication 6.82 ± 1.60 7.05 ± 1.60 6.17 ± 1.41 <.001 * Oxytocin agument, n(%) 230 (45.36) 171 (45.84) 59 (44.03) 0.717 Early Rupture of Membranes, n(%) 56 (11.05) 48 (12.87) 8 (5.97) 0.029 * Intrapartum fever, n(%) 12 (2.37) 9 (2.41) 3 (2.24) 1 Postpartum blood loss, M (Q₁, Q₃) 205(155.0,361.5) 185(150,278) 397.5(196.25,420) <.001 * Variables at birth Gestational age at delivery(wk) 39.43 ± 1.16 39.44 ± 1.15 39.40 ± 1.18 0.714 Neonatal weight(kg) 2595.37 ± 206.66 2605.42 ± 195.89 2567.40 ± 232.55 0.068 1min Apgar score 9.89 ± 0.65 9.90 ± 0.62 9.85 ± 0.71 0.419 5min Apgar＜7, n(%) 3 (0.59) 2 (0.54) 1 (0.75) 1 NICU, n(%) 187 (36.88) 138 (37.00) 49 (36.57) 0.929 Small for gestational age, n(%) 0.147 mild 418 (82.45) 313 (83.91) 105 (78.36) severe 89 (17.55) 60 (16.09) 29 (21.64) *P≤0.05. GDM:gestational diabetes mellitus; BMI:body mass index;NICU: Neonatal Intensive Care Units. Table 2 Demographic and Clinical Characteristics of Patients With SGA Undergoing Induction of Labor Variables Training set (N = 354) Validation set (N= 153) P Maternal age(y) 28.38 ± 4.39 29.07 ± 4.28 0.104 Maternal age, n(%) 0.293 ＜35 323 (91.24) 135 (88.24) ≥35 31 (8.76) 18 (11.76) Prepregnancy weight（kg） 50.91 ± 7.63 49.33 ± 6.48 0.026 * Prepregnancy BMI（kg/m2） 20.02 ± 2.80 19.79 ± 2.36 0.386 Prepregnancy BMI，n(%) 0.79 Underweight(BMI < 18.5) 110 (31.07) 44 (28.76) Normal(18.5 ≤ BMI < 25) 215 (60.73) 97 (63.40) Overweight(25.0 ≤ BMI < 30) 26 (7.34) 12 (7.84) Obese (BMI ≥ 30.0) 3 (0.85) 0 (0.00) Height（cm） 157.82 ± 5.09 159.44 ± 5.12 0.001 * Parity , n(%) 0.896 0 268 (75.71) 115 (75.16) ≥1 86 (24.29) 38 (24.84) Gravidity 1.69 ± 0.88 1.59 ± 0.98 0.297 Admission weight（kg） 60.53 ± 9.01 61.52 ± 9.44 0.269 Admission BMI（kg/m2） 24.28 ± 3.38 24.20 ± 3.55 0.812 Admission BMI (%) 0.586 Underweight(BMI < 18.5) 22 (6.21) 5 (3.27) Normal(18.5 ≤ BMI < 25) 145 (40.96) 65 (42.48) Overweight(25.0 ≤ BMI < 30) 141 (39.83) 64 (41.83) Obese (BMI ≥ 30.0) 46 (12.99) 19 (12.42) Estimated fetal weight（kg） 2662.67±264.79 2621.35±269.23 0.109 Bishop score before labor induction 2.85 ± 0.86 2.84 ± 0.86 0.952 Oxytocin agument, n(%) 157 (44.35) 73 (47.71) 0.49 Gestational age at adimission（wk) 38.96 ± 1.23 39.01 ± 1.18 0.709 Gestational age at delivery(wk） 39.42 ± 1.19 39.44 ± 1.15 0.885 GDM, n(%) 54 (15.25) 20 (13.07) 0.523 Gestational hypertension, n(%) 23 (6.50) 11 (7.19) 0.775 Oligoamnios, n(%) 43 (12.15) 16 (10.46) 0.586 Method of inducing labor, n(%) 0.836 cook double balloon 221 (62.43) 97 (63.40) dinoprostone 133 (37.57) 56 (36.60) Bishop score after medication 6.84 ± 1.58 6.81 ± 1.61 0.834 Oxytocin agument, n(%) 157 (44.35) 73 (47.71) 0.485 Delivery model, n(%) 0.451 vaginal delivery 257 (72.60) 116 (75.82) cesarean section 97 (27.40) 37 (24.18) Early Rupture of Membranes, n(%) 44 (12.43) 12 (7.84) 0.13 Intrapartum fever, n(%) 9 (2.54) 3 (1.96) 0.938 Postpartum blood loss M (Q₁, Q₃) 205.0(150.0,325.0) 202.5(158.5,373.7) 0.516 Neonatal weight（kg） 2590.86±227.16 2597.32±197.45 0.747 1min Apgar score 9.89 ± 0.71 9.87 ± 0.56 0.854 5min Apgar score＜7, n(%) 2 (0.56) 1 (0.65) 1 NICU, n(%) 141 (39.83) 46 (30.07) 0.036 * * P≤0.05. GDM:gestational diabetes mellitus; BMI:body mass index;NICU: Neonatal Intensive Care Units. Predictive Variable Screening Following univariate logistic regression analysis in the training set, several factors emerged with a p-value < 0.05 .There were then integrated into the logistic regression model,including maternal age, prepregnancy weight, weight at admission,body mass index (BMI) at admission, utilization of dinoprostone for induction, and Bishop score after cervical ripening. The independent risk factors linked to cesarean delivery following labor induction were uncovered through subsequent backward stepwise multivariate logistic regression analysis,as depicted in Table3. Four significant risk factors were identified: maternal age, admission weight, the use of dinoprostone for labor induction, and the Bishop score after medication. Notably, for each 1-point increase in Bishop score after medication, the odds of cesarean delivery decreased by 35% (aOR 0.65, 95% CI 0.54 - 0.80). Increments of 1 kg in admission weight and 1 year in maternal age corresponded to a 4.0% (aOR 1.04, 95% CI 1.01-1.07) and an 8.0% (aOR 1.08, 95% CI 1.01-1.15) rise in the likelihood of cesarean delivery, respectively. Furthermore, when comparing dinoprostone with the Cook double balloon method for labor induction, dinoprostone emerged as a notable risk factor, showing more than double the risk of cesarean delivery (aOR 2.08, 95% CI 1.13-3.81). Table 3 Independent risk factors for cesarean delivery among patients with SGA undergoing induction of labor at term in training set. Risk factors Unadjusted OR(95%CI) P Adjusted OR(95%CI) P Antenatal factors Maternal age 1.11 (1.04 ~ 1.18) 0.001 * 1.08 (1.01 ~ 1.15) 0.024 * Parity 0 1.00 (Reference) ≥1 1.09 (0.58 ~ 2.05) 0.79 Gravidity 1.12 (0.89 ~ 1.42) 0.328 Gestational age at adimission 0.96 (0.77 ~ 1.19) 0.721 Height 1.04 (0.98 ~ 1.09) 0.192 Prepregnancy weight 1.04 (1.01 ~ 1.08) 0.041 * 1.02 (0.99 ~ 1.05) 0.246 Prepregnancy BMI 1.08 (0.98 ~ 1.20) 0.137 Prepregnancy BMI Underweight(BMI < 18.5) 0.58 (0.32 ~ 1.06) 0.078 Normal(18.5 ≤ BMI < 25.0) 1.00 (Reference) Overweight(25.0 ≤ BMI < 30.0) 0.94 (0.32 ~ 2.76) 0.903 Obese (BMI ≥ 30.0) 0.00 (0.00 ~ Inf) 0.988 Admission weight 1.05 (1.02 ~ 1.08) <.001 * 1.04 (1.01 ~ 1.07) 0.027 * Admission BMI 1.12 (1.04 ~ 1.21) 0.003 * Admission BMI Underweight(BMI < 18.5) 0.59 (0.13 ~ 2.73) 0.498 0.53 (0.15 ~ 1.93) 0.338 Normal(18.5 ≤ BMI < 25.0) 1.00 (Reference) 1.00 (Reference) Overweight(25.0 ≤ BMI < 30.0) 2.87 (1.58 ~ 5.20) <.001 * 2.58 (1.32 ~ 5.06) 0.078 Obese (BMI ≥ 30.0) 1.96 (0.83 ~ 4.63) 0.124 0.94 (0.32 ~ 2.76) 0.903 Gestational diabetes mellitus 0.53 (0.24 ~ 1.19) 0.122 Gestational hypertension 0.90 (0.32 ~ 2.54) 0.838 Oligoamnios 1.36 (0.61 ~ 3.03) 0.446 Bishop score before labor induction 0.93 (0.69 ~ 1.26) 0.646 Estimated fetal weight 1.00 (1.00 ~ 1.00) 0.716 Intrapartum factors Gestational age at delivery 0.99 (0.79 ~ 1.23) 0.902 Method of inducing labor cook double balloon 1.00 (Reference) dinoprostone 2.00 (1.17 ~ 3.43) 0.011 * 2.08 (1.13 ~ 3.81) 0.018 * Bishop score after medication 0.69 (0.57 ~ 0.83) <.001 * 0.65 (0.54 ~ 0.80) <.001 * Oxytocin agument 1.23 (0.73 ~ 2.08) 0.445 Early rupture of membranes, n(%) 0.48 (0.16 ~ 1.42) 0.183 Intrapartum fever, n(%) 0.96 (0.19 ~ 4.88) 0.964 * P≤0.05,OR: Odds Ratio, CI: Confidence Interval Development of the Nomogram Utilizing the findings from the logistic regression analysis, we constructed a predictive nomogram for SGA patients undergoing labor induction. This model incorporates four predictive variables: maternal age, admission weight, the use of dinoprostone as an induction method, and the Bishop score after cervical ripening, along with the outcome variable of cesarean delivery (Figure 2).The individual scores for each predictive factors were added together, suggesting that a high overall score implies a greater likelihood of cesarean birth. Model Performance in the Training Set The prognostic efficacy of our model was performed within the training cohort. Discrimination is defined as the model's capacity to distinguish between events and non-events, measured by the area under the curve (AUC). As illustrated in Figure 3A, the nomogram achieved an AUC of 0.78 (95% CI: 0.73–0.84) in the training cohort, reflecting robust discriminative ability. With an optimal cutoff value of 36%, the nomogram demonstrated an accuracy of 0.79 (95% CI: 0.75–0.83), sensitivity of 0.86 (95% CI: 0.82–0.91), specificity of 0.61 (95% CI: 0.51–0.71), a positive predictive value (PPV) of 0.85 (95% CI: 0.81–0.90), and a negative predictive value (NPV) of 0.63 (95% CI: 0.53–0.73). Calibration refers to the degree of alignment between predicted probabilities and actual outcomes. The calibration plots in the training cohort reveal that the predictions of the nomogram are strongly correlated with the data observed(P=0.397)(Figure 3B). We further applied decision curve analysis (DCA) to assess alternative prognostic strategies, illustrating that the model conferred higher net benefits across probability thresholds from 5% to 80%, highlighting its practical applicability in clinical decision-making within this probability range. Performance of the model in the validation set The prediction model was evaluated with our validation cohort. To assess discriminative ability, calibration accuracy, and clinical effectiveness of the nomogram, we employed a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). As demonstrated in Figure 4A, the AUC for the validation cohort stood at 0.77 (95% CI: 0.68–0.86), suggesting strong discriminative power in predicting outcomes. Nomogram accuracy was 0.75, sensitivity 0,84, and specificity 0.46.The calibration curve in the validation cohort closely mirrored the diagonal, evidencing robust calibration ability(P=0.812) (Figure 4B). Furthermore, the DCA curve remained consistently elevated across a broad spectrum of threshold probabilities (Figure 4C), indicating that our model provides noteworthy net benefits in forecasting the likelihood of cesarean delivery for SGA patients following labor induction. Discussion Main Findings This study identified crucial hazards linked to cesarean section following failure induction in pregnancies complicated by SGA fetuses.Maternal age, admission weight, use of dinoprostone for induction, and Bishop score after cervical ripening were found to be independent predictors of cesarean delivery. The developed nomogram, based on these parameters serves as a visual tool for assessing individualized risk, thereby enhancing clinical decision-making. The nomogram in the model group demonstrated an area under the curve (AUC) of 0.78 (95% CI: 0.73–0.84), while the validation cohort confirmed an AUC of 0.77 (95% CI: 0.68–0.86). These findings emphasize the nomogram's robust discriminative power and predictive accuracy, offering a comprehensive reference for developing clinical guidelines that inform medical decisions. Interpretation of Findings and Comparison with Existing Literature The nomogram incorporates risk factors that have been previously documented in various publications.Nwabuobi C[6] developed a prediction model for cesarean birth following labor induction in SGA patients based on maternal age, gestational age, and the initial method of induction. Our research verified the observation that advancing maternal age elevates the likehood of cesarean section following labor induction in SGA cases, consistent with existing literature highlighting a correlation between older maternal age and increased cesarean rates[10-11]. Attali E[12] reported a significant correlation between maternal age and the occurrence of cesarean delivery,observed in a dose-dependent manner with [aOR1.56,95% CI:1.39-1.76) and 2.53 (2.07-3.09)] in women aged between 35-40 y and those over 40 y. Our study revealed that mothers aged over 35 years are 1.58 times more likely to undergo cesarean delivery following induction failure in SGA pregnancies (aOR 2.58, 95% CI: 1.09-6.09). This heightened risk may stem from complications associated with advanced maternal age, which could indicate poorer placental function and vascularity[13]. Additionally, our analysis identified admission weight as a significant factor contributing to cesarean delivery, suggesting that higher body weight alters maternal metabolic status and physiological conditions, exacerbating potential maternal and fetal complications.Quach D [14]showed that higher BMI (aOR, 1.05 (95% CI, 1.03-1.08)) were correlated with an gradual rise in the likelihood of cesarean delivery following IOL.Study has shown that obesity (BMI >30 kg/m2) can be an independent risk factor for CS[15].While univariate logistic regression indicated a correlation between elevated BMI and increased cesarean risk, after adjusting for confounders, only admission weight remained statistically significant (aOR 1.04, 95% CI: 1.01 - 1.07). This suggests that while overweight status might influence outcomes, the unique characteristics of the Chinese population potentially limit the generalizability of this finding. Conventionally, Bishop score has served as the standard assessment for planning inductions .Our research indicated that there was no notable disparity in the Bishop score prior to labor induction among the groups. This might due to all cases presenting with an immature cervix, characteriaed by Bishop scores approximately 3. Previous studies have suggested that the Bishop score for the cervix is not a reliable predictor of labor induction effectiveness[16]. However, the cervical Bishop score exhibited a noteworthy positive predictive value subsequent to the use of a cervical ripening device. Lee DS et al [17] discovered that a favorable Bishop score subsequent to cervical ripening was correlated with a decreased rate of cesarean delivery,even after adjusting for parity and Bishop score at admission. Our study similarly found that Bishop score after cervical ripeing was associated with decrease rate of cesarean delivery (aOR0.65, 95% CI:0.54 ~ 0.80).However,this method is subjective and susceptible to significant variation between observers.It is proposed that using ultrasound score (USG) and cervical length as a whole method can be employed to predict the probability of cesarean delivery during induction[18-19]. Dinoprostone is widely approved for cervical ripening before labor induction.Familiari A [20]found that the dinoprostone group had a cesarean delivery rate of 18.1% (95% CI 9.9-28.3) due to induction failure, while the misoprostol group had a rate of 9.4% (95% CI 1.4-22.0), and the mechanical method group had a rate of 8.1% (95% CI 5.0-11.6) in SGA patients..Di Mascio D [21]demonstrated that the rate of cesarean birth (25.6% vs. 17.2%; p = 0.027), composite negative neonatal outcome (26.1% vs. 16.7%; p = 0.013) and NICU admission (16.9% vs. 5.6%; p < 0.001) was greater in pregnancies affected by late fetal growth restriction undergoing IOL with dinoprostone than those performed with mechanical methods.Our findings revealed that dinoprostone doubled the risk of cesarean section (aOR 2.08, 95% CI: 1.13-3.81) compared to the Cook double balloon method. This elevated risk may be attributed to the potential for uterine hyperstimulation following dinoprostone administration[22], particularly in SGA cases complicated by placental insufficiency, necessitating a prompt cesarean delivery.In our research, 318 patients chose cook doble balloon as method of labor induction with safety ,only 189 pregnancies chose dionprostone.Therefore,the safety aspect of dinoprostone usage in inducing labor in SGA pregnancies continues to be a matter of concern. Clinical and Research Implications The nomogram established in this study serve as an invaluable clinical tool for individual risk assessment in pregnancies affected by SGA. By integrating key predictive factors—including maternal age, admission weight, dinoprostone usage, and the Bishop score after cervical ripein—healthcare providers can personalize management strategies for SGA pregnancies. Early identification of high-risk patients empowers individuals to make informed decisions regarding their delivery plans and fosters shared decision-making between clinicians and patients. By presenting risk probabilities visually, the nomogram encourages expectant mothers to actively engage in discussions around their delivery options, potentially enhancing patient satisfaction and compliance with medical recommendations. Moreover, the nomogram's ability to accurately determine patients at risk for cesarean delivery facilitates improved resource allocation within healthcare systems. Hospitals can make necessary preparations in advance, ensuring adequate surgical and neonatal care resources are available when needed. Such proactive measures not only enhance patient safety but also optimize operational efficiency within obstetric care facilities. Noneetheless,it’s important to realize that the nomogram is designed to assist in patients consultation,not to directly make clinical decisions. Study Strengths and Limitations This research has numerous significant advantages such as: (1) a fairly substantial sample size, (2) random division of the group into model creation and validation groups, and (3) the inclusion of easily measurable antepartum variables in the nomogram, making it practical for daily clinical use. However, There are several limitations to our study. As a retrospective, single-center investigation, there may be biases impacting the results. The findings may lack general applicability, as the specific characteristics of our patient population and institutional practices may not be reflective of broader hospital settings. Consequently, the identified risk factors may differ in varied contexts, necessitating validation in larger, multi-center cohorts. Additionally, while we examined key predictors, numerous other factors influencing labor outcomes in SGA pregnancies were not accounted for, such as psychological aspects, socioeconomic status, prenatal care differences, and genetic factors. The exclusion of these variables may limit the comprehensiveness of our predictive model. Finally, further external validation of the nomogram is necessary before widespread implementation in clinical practice. Future research should prioritize multi-center prospective studies to validate our findings and explore the nomogram's applicability in diverse clinical settings. Conclusion Our study validates the predictive capabilities of identified factors for cesarean delivery risk following induction failure in SGA patients through the development of a nomogram. This tool enhances the identification of high-risk patients and supports individualized management. Subsequent investigations should focus on optimizing the utilization of these risk factors to enhance clinical outcomes, reduce cesarean delivery rates, and promote maternal and neonatal well-being. Declarations Author contributions All authors helped to perform the research; Mingxing Yan: project development, data analysis, manuscript writing; Liping Hu: projecti development,data collection;Liying Li: Writing-Reviewing and Editing;Mengting Chen:data collection;Jun Shi:Data preparation; Jinji Wang: Investigation;data collection.Feng Li:Writing-Reviewing and Editing.All authors read and approved the final manuscript. Conflict of interest All authors declare that there was no conflict of interest. Funding Innovation Platform Project of Science and Technology,Fujian province（2021Y2012） National Key Clinical Specialty Construction Program of China（Obstetric） Ethical approval This study adheres to the principles of the Declaration of Helsinki (2000) of the World Medical Association. Ethics approval was obtained from the Ethics Review Committee of Fujian Maternal and Child Health Hospital [2023KY016]. As this is a retrospective study, the Institutional Review Board waived the need for individual written informed consent from the patients. Consent to participate All patients provided signed informed consent for labor induction. Written informed consent for study participation was obtained from all women included in the data analysis. Availability of data and materials The datasets utilized or analyzed during the current study are accessible from the corresponding author upon reasonable request. Acknowledgements Not applicable. References Wilcox AJ, Cortese M, McConnaughey DR, et al.The limits of small-for-gestational-age as a high-risk category[J]. Eur J Epidemiol, 2021, 36(10): 985-991. Castagno M, Menegon V, Monzani A, et al.. Small-for-gestational-age birth is linked to cardiovascular dysfunction in early childhood[J]. Am Heart J, 2019, 217: 84-93. Sacchi C, Marino C, Nosarti C, et al.. Association of intrauterine growth restriction and small for gestational age status with childhood cognitive outcomes: a systematic review and meta-analysis[J]. JAMA Pediatr, 2020, 174(8): 772-781. McCowan LM, Figueras F, Anderson NH. Evidence-based national guidelines for the management of suspected fetal growth restriction:comparison, consensus, and controversy. Am J Obstet Gynecol. 2018 Feb; 218(2 2S):S855–68. Parisi S, Monzeglio C, Attini R, et al. Evidence of lower oxygen reserves during labour in the growth restricted human foetus: a retrospective study. BMC Pregnancy Childbirth. 2017;17(1):209. 6.Nwabuobi C, Gowda N, Schmitz J, Wood N, Pargas A, Bagiardi L, Odibo L, Camisasca-Lopina H, Kuznicki M, Sinkey R, et al. Risk factors for Cesarean delivery in pregnancy with small-for-gestational-age fetus undergoing induction of labor. Ultrasound Obstet Gynecol. 2020 Jun;55(6):799-805. Kalafat E, Morales-Rosello J, Thilaganathan B, Tahera F, Khalil A. Risk of operative delivery for intrapartum fetal compromise in small-for-gestational-age fetuses at term: an internally validated prediction model. Am J Obstet Gynecol 2018; 218: 134.e131-134.e138. Simeone S, Marchi L, Canarutto R, Pina Rambaldi M, Serena C, Servienti C, Mecacci F.Doppler velocimetry and adverse outcome in labor induction for late IUGR. J Matern Fetal Neonatal Med 2017; 30: 323-328. Khalil A, Morales-Rosello J, Khan N, Nath M, Agarwal P, Bhide A, Papageorghiou A, Thilaganathan B. Is cerebroplacental ratio a marker of impaired fetal growth velocity and adverse pregnancy outcome? Am J Obstet Gynecol 2017; 216: 606.e601-606.e610. Rossi RM, Requarth E, Warshak CR, Dufendach KR, Hall ES, DeFranco EA. Risk calculator to predict cesarean delivery among women undergoing induction of labor. Obstet Gynecol. 2020;135(3):559–68. Pinton A, Lemaire Tomzack C, Merckelbagh H, Goffinet F. Induction of labour with unfavourable local conditions for suspected fetal growth restriction after 36 weeks of gestation: Factors associated with the risk of caesarean. J Gynecol Obstet Hum Reprod. 2021 Sep;50(7):101996. Attali E, Doleeb Z, Hiersch L, Amikam U, Gamzu R, Yogev Y, Ashwal E. The risk of intrapartum cesarean delivery in advanced maternal age. J Matern Fetal Neonatal Med. 2022 Dec;35(25):8019-8026. Lean SC, Derricott H, Jones RL, Heazell AEP. Advanced maternal age and adverse pregnancy outcomes: A systematic review and meta-analysis. PLoS One 2017; 12:e0186287. Quach D, Ten Eikelder M, Jozwiak M, Davies-Tuck M, Bloemenkamp KWM, Mol BW, Li W. Maternal and fetal characteristics for predicting risk of Cesarean section following induction of labor: pooled analysis of PROBAAT trials. Ultrasound Obstet Gynecol. 2022 Jan;59(1):83-92. Nkoka O, Ntenda PAM, Senghore T, Bass P. Maternal overweight and obesity and the risk of caesarean birth in Malawi. Reprod Health. 2019 Apr 3;16(1):40. Winner RM, Graves J, Jarvis K, Beckman D, Youkilis BB, Monroe M, Davies CC. Relationships Among Mode of Birth, Onset of Labor, and Bishop Score. J Obstet Gynecol Neonatal Nurs. 2024 May 20:S0884-2175(24)00069-8. Lee DS, Tandel MD, Kwan L, Francoeur AA, Duong HL, Negi M. Favorable Simplified Bishop Score after cervical ripening associated with decreased cesarean birth rate. Am J Obstet Gynecol 2022;4(2):100534. Sinha P, Gupta M, Meena S. Comparing Transvaginal Ultrasound Measurements of Cervical Length to Bishop Score in Predicting Cesarean Section Following Induction of Labor: A Prospective Observational Study. Cureus. 2024 Feb 16;16(2):e54335. Manchu M, Redla V. Prediction of mode of delivery by an ultrasound score similar to Bishop score and performance of independent predictors. J Ultrasound. 2023 Sep;26(3):619-626. Familiari A, Khalil A, Rizzo G, Odibo A, Vergani P, Buca D, Hidaka N, Di Mascio D, Nwabuobi C, Simeone S, et al. Adverse intrapartum outcome in pregnancies complicated by small for gestational age and late fetal growth restriction undergoing induction of labor with Dinoprostone, Misoprostol or mechanical methods: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2020 Sep;252:455-467. Di Mascio D, Villalain C, Rizzo G, Morales‐Rosello J, Sileo FG, Maruotti GM, Prefumo F, Galindo A, D'Antonio F; induCtion of labOr in Late fetaL Growth rEstriction (COLLEGE) Study Group. Maternal and neonatal outcomes of pregnancies complicated by late fetal growth restriction undergoing induction of labor with dinoprostone compared with cervical balloon: A retrospective, international study. Acta Obstet Gynecol Scand. 2021 Jul;100(7):1313-1321. Shirley M. Dinoprostone vaginal insert: a review in cervical ripening. Drugs.2018;78(15):1615–1624. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4892379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351536523,"identity":"1ab3a0e5-1298-47c6-a069-14779fa114f7","order_by":0,"name":"Mingxing Yan","email":"","orcid":"","institution":"Fujian Medical University, National Key Obstetric Clinical Specialty Construction Institution of China","correspondingAuthor":false,"prefix":"","firstName":"Mingxing","middleName":"","lastName":"Yan","suffix":""},{"id":351536525,"identity":"80544e3c-d50d-418d-b211-916147f6a6c4","order_by":1,"name":"Liping Hu","email":"","orcid":"","institution":"Fujian Medical University, National Key Obstetric Clinical Specialty Construction Institution of China","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Hu","suffix":""},{"id":351536526,"identity":"8bffe50a-1060-492d-838e-58e81466369e","order_by":2,"name":"Mengting Chen","email":"","orcid":"","institution":"Fujian Medical University, National Key Obstetric Clinical Specialty Construction Institution of China","correspondingAuthor":false,"prefix":"","firstName":"Mengting","middleName":"","lastName":"Chen","suffix":""},{"id":351536528,"identity":"0770f48c-838f-4f17-89a0-8533ff110430","order_by":3,"name":"Jun Shi","email":"","orcid":"","institution":"Fujian Medical University, National Key Obstetric Clinical Specialty Construction Institution of China","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Shi","suffix":""},{"id":351536530,"identity":"00799c59-9b66-4d6e-8bb5-2338422f9d41","order_by":4,"name":"Feng Li","email":"","orcid":"","institution":"Fujian Medical University, National Key Obstetric Clinical Specialty Construction Institution of China","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Li","suffix":""},{"id":351536531,"identity":"c836a096-aaa3-4420-9bb8-f3c7bc30c27c","order_by":5,"name":"jinji Wang","email":"","orcid":"","institution":"Fujian Medical University, National Key Obstetric Clinical Specialty Construction Institution of China","correspondingAuthor":false,"prefix":"","firstName":"jinji","middleName":"","lastName":"Wang","suffix":""},{"id":351536532,"identity":"b21f37db-a8f7-4859-afc9-2626681d126f","order_by":6,"name":"Liying Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACA2YGhgNAWoaNvfGBAVjoAJFaeNh4DhsQqQVK8zBIJEPZBLWw8xgeLvjFwMMn+Zih6GYbgxzfjQTGzwV4HcZjcHhmH9Bh0skMxrltDMaSNxKYpWcQ0sLbA9KSfwCkJXHDjQQ2Zh6itEgeBttST5wWnh9ALRLMYC0JBoS1sBUc5m0ABTLQLznnJAxnnnnYLI1Pi33/4c2fef4wyMm3H2Yzzimzkec7nnzwMz4tDAwcBgyMbf9BLDZgxEgAacYGvBoYGNgfMDD8AbOYHxBQOgpGwSgYBSMUAAByhT+1RQpmwgAAAABJRU5ErkJggg==","orcid":"","institution":"Fujian Medical University, National Key Obstetric Clinical Specialty Construction Institution of China","correspondingAuthor":true,"prefix":"","firstName":"Liying","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-08-10 15:40:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4892379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4892379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66329015,"identity":"d6ba6476-f23c-46e5-8725-4a152f21fbc7","added_by":"auto","created_at":"2024-10-10 13:10:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171963,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4892379/v1/283cdde18caf83da4aed714c.png"},{"id":66329542,"identity":"88f7ea9c-78f7-452a-9083-bec8939e0280","added_by":"auto","created_at":"2024-10-10 13:18:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22401,"visible":true,"origin":"","legend":"\u003cp\u003eA clinical feature model was used to develop a nomogram.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4892379/v1/1dd1e02a0db772ddd698aadb.png"},{"id":66329011,"identity":"3514f6f0-e7aa-492b-8df3-18af79de866e","added_by":"auto","created_at":"2024-10-10 13:10:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44191,"visible":true,"origin":"","legend":"\u003cp\u003eModel discrimination and performance in the training set. (A) Receiver operating characteristic curves (AUC) for nomogram-based prognostic prediction. (B) Calibration plot examining estimation accuracy. (C) Decision curve analyses(DCA) assessing clinical utility.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4892379/v1/325c2e27398d710ba3878e0f.png"},{"id":66329013,"identity":"ab093eaf-74f7-46cc-89f9-0445f3d78b7c","added_by":"auto","created_at":"2024-10-10 13:10:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38356,"visible":true,"origin":"","legend":"\u003cp\u003eModel discrimination and performance in the validation set. (A) Receiver operating characteristic curves for nomogram-based prognostic prediction. (B) Calibration plot examining the estimation accuracy. (C) Decision curve analyses assessing clinical utility.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4892379/v1/85453af994e97886a68d0797.png"},{"id":80030865,"identity":"ae5be16e-8431-49ff-950a-b7d00d495abf","added_by":"auto","created_at":"2025-04-07 07:32:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1361559,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4892379/v1/0ca767c5-bea8-46f4-bae0-14ad00312aa0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmall for gestational age (SGA) refers to infants whose birth weight is below the 10th percentile for their same gestational age and sex [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].As a high-risk group among newborns, SGA infants are predisposed to various perinatal diseases and mortality. They are also susceptible to multiple long-term metabolic and cardiovascular conditions. Moreover, they face significantly increased risks of delayed growth, developmental delays, and neurological disorders, profoundly affecting their quality of life in long- and short-term[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to the heightened risk of stillbirth in pregnancies beyond 37 weeks of gestation, there is a common consensus advocating for labor induction during the early term phase [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, the incidence of cesarean delivery remains notably high in this context, primarily due to the associated risks of intrapartum fetal acidosis and fetal heart rate (FHR) abnormalities commonly seen in SGA fetuses[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have identified several risk factors contributing to cesarean delivery[6\u0026ndash;9], including Doppler abnormalities in the umbilical artery, a reduced cerebroplacental ratio (CPR), oligohydramnios, the utilization of prostaglandins, and non-reassuring fetal heart tracing(NRFHT). Determining the appropriate timing and method of delivery for fetuses with SGA poses considerable challenges across all stages of gestational age. Thus, it is vital to identify antenatal and intrapartum risk factors associated with cesarean delivery at labor induction.\u003c/p\u003e \u003cp\u003eThe objective of this research is to develop and validate an objective model for predicting cesarean deliveries due to failed labor inductions in pregnancies complicated by the SGA in term singletons.This model seeks to enhance informed decision-making, optimize resource allocation, and mitigate the potential risks associated with failed labor induction.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA retrospective case-control study was carried out at Fujian Maternity and Child Health Hospital, involving a series of pregnant women diagnosed as having small for gestational age fetuses between October 2017 and December 2022. Ethical approval was obtained for the study from the hospital\u0026rsquo;s Ethics Committee (reference number 2023KY016). Comprehensive maternal and neonatal characteristics were systematically collected from medical records. Considering the study\u0026rsquo;s retrospectinve appoach and the anonymous date of the patients, obtaining informed consent was deemed unnecessary.\u003c/p\u003e \u003cp\u003eThe initial inclusion criteria included singleton pregnancies diagnosed with SGA, characterized by a birth weight that falls under the 10th percentile at term (\u0026ge;\u0026thinsp;37 weeks). Exclusion criteria comprised multiple pregnancies, congenital malformations, fetal chromosomal abnormalities, premature rupture of membranes (PROM), prior uterine surgery (such as cesarean section or myomectomy), and cases lost to follow-up. In total, 507 cases were evaluated for labor induction, all presenting cephalically with a Bishop score\u0026thinsp;\u0026lt;\u0026thinsp;6 for cervical ripening. Figure\u0026nbsp;1 depicts the selection procedure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTreatment Protocol for SGA Pregnancies and Induction Methods\u003c/h2\u003e \u003cp\u003eIn accordance with departmental protocols, all SGA patients underwent routine ultrasound and fetal heart rate monitoring prior to labor induction. A senior physician assessed the cervical condition via the Bishop score. Patients were thoroughly informed about the induction procedure and its potential risks, including those related to the use of Cook's double balloon and dinoprostone. Each SGA patient provided informed consent, selecting their preferred method for cervical ripening. Following manufacturer guidelines, 10mg of dinoprostone was introduced into the posterior vaginal fornix, while 80ml of saline was used to inflate both intrauterine and intravaginal balloons for the Cook's double balloon.\u003c/p\u003e \u003cp\u003eIn our labor induction protocol, each method was applied for a maximum of 12 hours, accompanied by intermittent cardiotocographic monitoring. Criteria for removal included reaching the active phase of labor (\u0026ge;\u0026thinsp;2cm cervical dilation), membrane rupture, non-reassuring fetal heart rate, placental abruption, or uterine tachysystole (\u0026gt;\u0026thinsp;5 contractions in 10 minutes) for either device. If active labor (\u0026ge;\u0026thinsp;2cm cervical dilation) was not achieved within 12 hours, the devices were removed, and intravenous oxytocin was initiated at a rate of 2.5 mIU/min, with adjusted every 15 minutes based on uterine contractions. Women achieving vaginal delivery following induction were classified as successful, whereas those requiring cesarean delivery for any reason were considered failed inductions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eThe primary outcome was to identify risk factors linked to cesarean birth in pregnancies complicated by SGA during labor induction. The analyzed factors included maternal demographic characteristics such as age, height, weight, body mass index (BMI), gravidity, and parity;complications during pregnancy like gestational diabetes, pregnancy-induced hypertension and oligohydramnios, obstetric conditions including gestational age, Bishop scores before and after cervical ripening, induction methods, oxytocin augmentation, and mode of delivery, complications during labor such as fever, early rupture of membranes, and blood loss after birth, and neonatal characteristics like estimated fetal weight, neonatal weight, Apgar scores at 1 and 5 minutes, and admission to the neonatal intensive care unit (NICU)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eNumerical variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), whereas categorical variables were expressed as frequencies and percentages.The Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U-test was employed for comparing continuous variables, while the chi-square (χ\u0026sup2;) test or Fisher\u0026rsquo;s exact test was utilized for categorical variables.Risk factors correlated with cesarean delivery were identified through multivariate binary logistic regression analysis, applying univariate predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) via a stepwise backward elimination method. In the multivariate analysis,only variables that had a p-value of less than 0.05 in the univariate analysis were included,whereas insignificant factors were omitted. Instances of multicollinearity necessitated the retention of only one variable within the model.All statistical tests were performed as two-tailed, considering p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant. Associations were expressed as odds ratios (OR),accompanied by 95% confidence intervals (CIs). A graphical nomogram was generated to visually represent the logistic regression model.\u003c/p\u003e \u003cp\u003eThe model\u0026rsquo;s discriminatory performance was quantified by the area under the receiver operating characteristic (ROC) curve (AUC). The ROC curve was derived from the regression model, with both the AUC and its 95% CIs reported. AUC values exceeding 0.7 suggest robust discriminatory capacity. Calibration was evaluated using the Hosmer-Lemeshow test, accompanied by a calibration curve illustrating predicted versus actual probabilities, with alignment closely following the ideal 45\u0026deg; line indicating strong correlation. Clinical applicability was assessed via decision curve analysis (DCA). All statistical analyses were executed using R (version 4.3.0; The R Project for Statistical Computing, Vienna, Austria; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and SPSS (version 24.0; IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Patient Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participant selection flowchart for our case-control study is illustrated in Figure 1. Of 998 patients meeting inclusion criteria, 491 were excluded due to factors such as multiple pregnancies, chromosomal abnormalities, and major malformations. Ultimately, a cohort of 507 participants was included in this study, with 354 (70%) experiencing SGA complications enrolled as the derivation cohort and 153 (30%) included in the validation set. A total of 134 (26.43%) women in the cohort underwent cesarean delivery following labor induction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs depicted in Table 1,a comparison was made between the maternal, neonatal, and obstetric characteristics\u0026nbsp;of women\u0026nbsp;who achieved vaginal delivery and those who required cesarean\u0026nbsp;section. Women who underwent cesarean section demonstrated higher maternal age (29.68 \u0026plusmn; 4.58 vs. 28.19 \u0026plusmn; 4.22, P \u0026lt; 0.001), greater admission weight (62.81 \u0026plusmn; 8.39 vs. 60.65 \u0026plusmn; 9.57, P = 0.015), and elevated admission BMI (24.84 \u0026plusmn; 2.93 vs. 24.00 \u0026plusmn; 3.66, P = 0.008) in comparison to vaginal delivery patients. Cesarean deliveries were also associated with lower Bishop scores following cervical ripening (6.17 \u0026plusmn; 1.41 vs. 7.05 \u0026plusmn; 1.60, P \u0026lt; 0.001),\u0026nbsp;An increased incidence\u0026nbsp;of oligohydramnios (16.42% vs. 9.92%, P = 0.044), lower rates of early rupture of membranes during induction (5.97% vs. 12.87%, P = 0.029), and increased postpartum blood loss (397.5 ml vs. 185 ml, P \u0026lt; 0.001). Notably, the utilization of dinoprostone for induction resulted in a statistically significant cesarean delivery rate of 47.01% among SGA patients when compared to the Cook double balloon method. Table 2 provides the characteristics of pregnancies in both the derivation and validation cohorts, which shared similar maternal and fetal features, indicating the robustness of random group assignment.\u003c/p\u003e\n\u003cp\u003eTable 1 Univariate analysis of demographic and clinical characteristics of pregnancies with SGA stratified by mode of delivery (N=507)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N= 507)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\" rowspan=\"2\"\u003e\n \u003cp\u003evaginal delivery\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N= 373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\" rowspan=\"2\"\u003e\n \u003cp\u003ecesarean delivery\u003c/p\u003e\n \u003cp\u003e(N = 134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"34\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"34\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntenatal variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eMaternal age(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e28.59 \u0026plusmn; 4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e28.19 \u0026plusmn; 4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e29.68 \u0026plusmn; 4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eMaternal age, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e＜35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e458 (90.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e345 (92.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e113 (84.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e49 (9.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e28 (7.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e21 (15.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eGravidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1.62 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1.62 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1.63 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eGestational age at adimission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e38.99 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e39.00 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e38.96 \u0026plusmn; 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eHeight（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e158.95 \u0026plusmn; 5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e158.95 \u0026plusmn; 5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e158.95 \u0026plusmn; 5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003ePrepregnancy weight（kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e50.44 \u0026plusmn; 7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e50.17 \u0026plusmn; 7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e51.17 \u0026plusmn; 7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003ePrepregnancy BMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e19.95 \u0026plusmn; 2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e19.85 \u0026plusmn; 2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e20.22 \u0026plusmn; 2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003ePrepregnancy BMI，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eUnderweight(BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e154 (30.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e121 (32.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e33 (24.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eNormal(18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e312 (61.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e222 (59.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e90 (67.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eOverweigt(25.0\u0026thinsp;\u0026le;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e38 (7.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e28 (7.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e10 (7.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e3 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAdmission weight(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e61.22 \u0026plusmn; 9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e60.65 \u0026plusmn; 9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e62.81 \u0026plusmn; 8.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAdmission BMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e24.23 \u0026plusmn; 3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e24.00 \u0026plusmn; 3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e24.84 \u0026plusmn; 2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eAdmission BMI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eUnderweight(BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e27 (5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e25 (6.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2 (1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eNormal(18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e210 (41.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e165 (44.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e45 (33.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eOverweight(25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e205 (40.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e135 (36.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e70 (52.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e65 (12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e48 (12.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e17 (12.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eParity , n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e383 (75.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e277 (74.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e106 (79.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e124 (24.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e96 (25.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e28 (20.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eGDM, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e74 (14.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e58 (15.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e16 (11.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eGestational hypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e34 (6.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e27 (7.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e7 (5.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eOligoamnios, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e59 (11.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e37 (9.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e22 (16.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eBishop score before labor induction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2.85 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2.88 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.76 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eEstimated fetal weight(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2650.20 \u0026plusmn; 266.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2655.31 \u0026plusmn; 249.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2635.96\u0026plusmn; 308.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntrapartum variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eMethod of inducing labor, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp; cook double balloon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e318 (62.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e247 (66.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e71 (52.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u0026nbsp; dinoprostone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e189 (37.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e126 (33.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e63 (47.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eBishop score after medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e6.82 \u0026plusmn; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e7.05 \u0026plusmn; 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e6.17 \u0026plusmn; 1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eOxytocin agument, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e230 (45.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e171 (45.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e59 (44.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"36\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eEarly Rupture of Membranes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e56 (11.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e48 (12.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e8 (5.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eIntrapartum fever, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e12 (2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e9 (2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3 (2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003ePostpartum blood loss,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eM (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e205(155.0,361.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e185(150,278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e397.5(196.25,420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables at birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eGestational age at delivery(wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e39.43 \u0026plusmn; 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e39.44 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e39.40 \u0026plusmn; 1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eNeonatal weight(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2595.37 \u0026plusmn; 206.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2605.42 \u0026plusmn; 195.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2567.40 \u0026plusmn; 232.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e1min Apgar score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e9.89 \u0026plusmn; 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e9.90 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e9.85 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003e5min Apgar＜7, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e3 (0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e2 (0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e1 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eNICU, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e187 (36.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e138 (37.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e49 (36.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003eSmall for gestational age, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003emild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e418 (82.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e313 (83.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e105 (78.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.927835051546392%\"\u003e\n \u003cp\u003esevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e89 (17.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e60 (16.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e29 (21.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*P\u0026le;0.05.\u0026nbsp;GDM:gestational diabetes mellitus; BMI:body mass index;NICU: Neonatal Intensive Care Units.\u003c/p\u003e\n\u003cp\u003eTable 2 Demographic and Clinical Characteristics of Patients With SGA Undergoing Induction of Labor\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" rowspan=\"2\"\u003e\n \u003cp\u003eTraining set\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N = 354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" rowspan=\"2\"\u003e\n \u003cp\u003eValidation set\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N= 153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"45\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"45\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMaternal age(y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e28.38 \u0026plusmn; 4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e29.07 \u0026plusmn; 4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMaternal age, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e＜35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e323 (91.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e135 (88.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e31 (8.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e18 (11.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003ePrepregnancy weight（kg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e50.91 \u0026plusmn; 7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e49.33 \u0026plusmn; 6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003ePrepregnancy BMI（kg/m2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e20.02 \u0026plusmn; 2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e19.79 \u0026plusmn; 2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"39\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003ePrepregnancy BMI，n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eUnderweight(BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e110 (31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e44 (28.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eNormal(18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e215 (60.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e97 (63.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eOverweight(25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e26 (7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e12 (7.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e3 (0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eHeight（cm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e157.82 \u0026plusmn; 5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e159.44 \u0026plusmn; 5.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eParity , n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e268 (75.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e115 (75.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e86 (24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e38 (24.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eGravidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e1.69 \u0026plusmn; 0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e1.59 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eAdmission weight（kg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e60.53 \u0026plusmn; 9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e61.52 \u0026plusmn; 9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eAdmission BMI（kg/m2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e24.28 \u0026plusmn; 3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e24.20 \u0026plusmn; 3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eAdmission BMI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eUnderweight(BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e22 (6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e5 (3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eNormal(18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e145 (40.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e65 (42.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eOverweight(25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e141 (39.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e64 (41.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e46 (12.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e19 (12.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eEstimated fetal weight（kg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2662.67\u0026plusmn;264.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2621.35\u0026plusmn;269.23\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eBishop score before labor induction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2.85 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2.84 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eOxytocin agument, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e157 (44.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e73 (47.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eGestational age at adimission（wk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e38.96 \u0026plusmn; 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e39.01 \u0026plusmn; 1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eGestational age at delivery(wk）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e39.42 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e39.44 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eGDM, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e54 (15.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e20 (13.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eGestational hypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e23 (6.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e11 (7.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eOligoamnios, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e43 (12.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e16 (10.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eMethod of inducing labor, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003ecook double balloon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e221 (62.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e97 (63.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e\u0026nbsp;dinoprostone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e133 (37.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e56 (36.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eBishop score after medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e6.84 \u0026plusmn; 1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e6.81 \u0026plusmn; 1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eOxytocin agument, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e157 (44.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e73 (47.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eDelivery model, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003evaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e257 (72.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e116 (75.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003ecesarean section\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e97 (27.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e37 (24.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eEarly Rupture of Membranes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e44 (12.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e12 (7.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eIntrapartum fever, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e9 (2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e3 (1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003ePostpartum blood loss\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;M (Q₁, Q₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e205.0(150.0,325.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e202.5(158.5,373.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eNeonatal weight（kg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2590.86\u0026plusmn;227.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2597.32\u0026plusmn;197.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e1min Apgar score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e9.89 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e9.87 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003e5min Apgar score＜7, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e2 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e1 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.775510204081634%\"\u003e\n \u003cp\u003eNICU, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e141 (39.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e46 (30.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"27\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003eP\u0026le;0.05.\u0026nbsp;GDM:gestational diabetes mellitus; BMI:body mass index;NICU: Neonatal Intensive Care Units.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Variable Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing univariate logistic regression analysis in the training set, several\u0026nbsp;factors\u0026nbsp;emerged with a p-value \u0026lt; 0.05\u0026nbsp;.There were then integrated into\u0026nbsp;the logistic regression model,including\u0026nbsp;maternal age, prepregnancy weight, weight\u0026nbsp;at\u0026nbsp;admission,body mass index (BMI)\u0026nbsp;at\u0026nbsp;admission, utilization of dinoprostone for induction, and Bishop score\u0026nbsp;after\u0026nbsp;cervical ripening.\u0026nbsp;The independent risk factors linked to cesarean delivery following labor induction were uncovered through subsequent backward stepwise multivariate logistic regression analysis,as depicted in Table3.\u0026nbsp;Four significant risk factors were identified: maternal age, admission weight, the use of dinoprostone for labor induction, and the Bishop score after medication. Notably, for each 1-point increase in Bishop score\u0026nbsp;after\u0026nbsp;medication, the odds of cesarean delivery decreased by 35% (aOR 0.65, 95% CI 0.54\u0026nbsp;-\u0026nbsp;0.80). Increments of 1 kg in admission weight and 1 year in maternal age corresponded to a 4.0% (aOR 1.04, 95% CI 1.01-1.07) and an 8.0% (aOR 1.08, 95% CI 1.01-1.15) rise in the likelihood of cesarean delivery, respectively. Furthermore, when comparing\u0026nbsp;dinoprostone with the Cook double balloon method for labor induction, dinoprostone\u0026nbsp;emerged\u0026nbsp;as a notable risk factor,\u0026nbsp;showing more than double the risk of\u0026nbsp;\u0026nbsp;cesarean delivery (aOR 2.08, 95% CI 1.13-3.81).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Independent risk factors for cesarean delivery among patients with SGA undergoing induction of labor at term\u0026nbsp;\u003cstrong\u003ein training set.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\" rowspan=\"2\"\u003e\n \u003cp\u003eRisk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\" rowspan=\"2\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\" rowspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\" rowspan=\"2\"\u003e\n \u003cp\u003eAdjusted OR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"34\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"34\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntenatal factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eMaternal age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.11 (1.04 ~ 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e1.08 (1.01 ~ 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eParity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.09 (0.58 ~ 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eGravidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.12 (0.89 ~ 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eGestational age at adimission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.96 (0.77 ~ 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.04 (0.98 ~ 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003ePrepregnancy weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.04 (1.01 ~ 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e1.02 (0.99 ~ 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003ePrepregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.08 (0.98 ~ 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003ePrepregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eUnderweight(BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.58 (0.32 ~ 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eNormal(18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eOverweight(25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.94 (0.32 ~ 2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.00 (0.00 ~ Inf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eAdmission weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.05 (1.02 ~ 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e1.04 (1.01 ~ 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eAdmission BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.12 (1.04 ~ 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eAdmission BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eUnderweight(BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.59 (0.13 ~ 2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e0.53 (0.15 ~ 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eNormal(18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eOverweight(25.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e2.87 (1.58 ~ 5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e2.58 (1.32 ~ 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.96 (0.83 ~ 4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e0.94 (0.32 ~ 2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eGestational diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.53 (0.24 ~ 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eGestational hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.90 (0.32 ~ 2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eOligoamnios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.36 (0.61 ~ 3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eBishop score before labor induction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.93 (0.69 ~ 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eEstimated fetal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.00 (1.00 ~ 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntrapartum factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eGestational age at delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.99 (0.79 ~ 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eMethod of inducing labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003e\u0026nbsp; cook double balloon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"20\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003e\u0026nbsp; dinoprostone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e2.00 (1.17 ~ 3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e2.08 (1.13 ~ 3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eBishop score after medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.69 (0.57 ~ 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e0.65 (0.54 ~ 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eOxytocin agument\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e1.23 (0.73 ~ 2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eEarly rupture of membranes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.48 (0.16 ~ 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.276729559748425%\"\u003e\n \u003cp\u003eIntrapartum fever, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.540880503144653%\"\u003e\n \u003cp\u003e0.96 (0.19 ~ 4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.81761006289308%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003eP\u0026le;0.05,OR: Odds Ratio, CI: Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.748427672955975%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.58490566037736%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.849056603773585%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"19\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of the Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing the findings from the logistic regression analysis, we constructed a predictive nomogram for SGA patients undergoing labor induction. This model incorporates four predictive variables: maternal age, admission weight, the use of dinoprostone as an induction method, and the Bishop score after cervical ripening, along with the outcome variable of cesarean delivery (Figure 2).The individual scores for each predictive factors were added together, suggesting that a high overall score implies a greater likelihood of cesarean birth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Performance in the Training Set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prognostic efficacy of our model was performed within the training cohort. Discrimination is defined as the model\u0026apos;s capacity to distinguish between events and non-events, measured by the area under the curve (AUC). As illustrated in Figure 3A, the nomogram achieved an AUC of 0.78 (95% CI: 0.73\u0026ndash;0.84) in the training cohort, reflecting robust discriminative ability. With an optimal cutoff value of 36%, the nomogram demonstrated an accuracy of 0.79 (95% CI: 0.75\u0026ndash;0.83), sensitivity of 0.86 (95% CI: 0.82\u0026ndash;0.91), specificity of 0.61 (95% CI: 0.51\u0026ndash;0.71), a positive predictive value (PPV) of 0.85 (95% CI: 0.81\u0026ndash;0.90), and a negative predictive value (NPV) of 0.63 (95% CI: 0.53\u0026ndash;0.73).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCalibration refers to the degree of alignment between predicted probabilities and actual outcomes. The calibration plots in the training cohort reveal that the predictions of the nomogram are strongly correlated with the data observed(P=0.397)(Figure 3B). We further applied decision curve analysis (DCA) to assess alternative prognostic strategies, illustrating that the model conferred higher net benefits across probability thresholds from 5% to 80%, highlighting its practical applicability in clinical decision-making within this probability range.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance of the model in the validation set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe prediction model was evaluated with our validation cohort. To assess discriminative ability, calibration accuracy, and clinical effectiveness of the nomogram, we employed a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). As demonstrated in Figure 4A, the AUC for the validation cohort stood at 0.77 (95% CI: 0.68\u0026ndash;0.86), suggesting strong discriminative power in predicting outcomes. Nomogram accuracy was 0.75, sensitivity 0,84, and specificity 0.46.The calibration curve in the validation cohort closely mirrored the diagonal, evidencing robust calibration ability(P=0.812) (Figure 4B). Furthermore, the DCA curve remained consistently elevated across a broad spectrum of threshold probabilities (Figure 4C), indicating that our model provides noteworthy net benefits in forecasting the likelihood of cesarean delivery for SGA patients following labor induction.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMain Findings\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identified\u0026nbsp;crucial hazards linked to\u0026nbsp;cesarean section following\u0026nbsp;failure\u0026nbsp;induction in pregnancies complicated by SGA\u0026nbsp;fetuses.Maternal age, admission weight, use of dinoprostone for induction, and Bishop score after cervical ripening were found to be independent predictors of cesarean delivery. The developed nomogram,\u0026nbsp;based\u0026nbsp;on these parameters\u0026nbsp;serves as a visual tool for assessing individualized risk, thereby enhancing clinical decision-making.\u0026nbsp;The nomogram in the model group\u0026nbsp;demonstrated an area under the curve (AUC) of 0.78 (95% CI: 0.73–0.84), while the validation cohort confirmed an AUC of 0.77 (95% CI: 0.68–0.86). These findings emphasize the nomogram's robust discriminative power and predictive accuracy, offering a\u0026nbsp;comprehensive reference\u0026nbsp;for developing clinical guidelines that inform medical decisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of Findings and Comparison with Existing Literature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nomogram incorporates risk factors that have been previously documented in various publications.Nwabuobi\u0026nbsp;C[6]\u0026nbsp;developed\u0026nbsp;a prediction model for cesarean\u0026nbsp;birth following\u0026nbsp;labor induction in SGA patients based on maternal age, gestational age, and the initial method of induction. Our research\u0026nbsp;verified\u0026nbsp;the observation that advancing maternal age elevates the\u0026nbsp;likehood\u0026nbsp;of cesarean\u0026nbsp;section\u0026nbsp;following labor induction in SGA cases, consistent with existing literature highlighting a correlation between older maternal age and increased cesarean rates[10-11]. Attali E[12]\u0026nbsp;reported a significant\u0026nbsp;correlation between\u0026nbsp;maternal age\u0026nbsp;and the occurrence of\u0026nbsp;cesarean delivery,observed in a dose-dependent manner\u0026nbsp;with\u0026nbsp;[aOR1.56,95% CI:1.39-1.76) and 2.53 (2.07-3.09)] in women aged between 35-40 y and those over 40 y. Our study revealed that mothers aged over 35 years are 1.58 times more likely to undergo cesarean delivery following induction failure in SGA pregnancies (aOR 2.58, 95% CI: 1.09-6.09). This heightened risk may stem from complications associated with advanced maternal age, which could indicate poorer placental function and vascularity[13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, our analysis identified admission weight as a significant factor contributing to cesarean delivery, suggesting that higher body weight alters maternal metabolic status and physiological conditions, exacerbating potential maternal and fetal complications.Quach D\u0026nbsp;[14]showed that\u0026nbsp;higher BMI (aOR, 1.05 (95% CI, 1.03-1.08)) were correlated with an gradual rise in the likelihood\u0026nbsp;of cesarean delivery\u0026nbsp;following IOL.Study has shown that obesity (BMI \u0026gt;30 kg/m2) can be an independent risk factor for CS[15].While univariate logistic regression indicated a correlation between elevated BMI and increased cesarean risk, after adjusting for confounders, only admission weight remained statistically significant (aOR 1.04, 95% CI: 1.01 - 1.07). This suggests that while overweight status might influence outcomes, the unique characteristics of the Chinese population potentially limit the generalizability of this finding.\u003c/p\u003e\n\u003cp\u003eConventionally,\u0026nbsp;Bishop score has served as the standard assessment for planning inductions .Our research indicated that there was no notable disparity in the Bishop score prior to labor induction among the groups. This might\u0026nbsp;due to\u0026nbsp;all cases presenting with an immature cervix,\u0026nbsp;characteriaed by Bishop\u0026nbsp;scores approximately\u0026nbsp;3. Previous studies have suggested that the Bishop score for the\u0026nbsp;cervix\u0026nbsp;is not a reliable predictor of labor induction effectiveness[16].\u0026nbsp;However, the cervical Bishop score exhibited a noteworthy positive predictive value subsequent to the use of a cervical ripening device.\u0026nbsp;Lee DS et al [17] discovered that a favorable Bishop score subsequent to cervical ripening was correlated with a decreased rate of cesarean delivery,even after adjusting for parity and Bishop score at admission. Our study similarly found that Bishop score after cervical ripeing was associated with decrease rate of cesarean delivery (aOR0.65, 95% CI:0.54 ~ 0.80).However,this method is subjective and susceptible to significant variation between observers.It is proposed that\u0026nbsp;using ultrasound\u0026nbsp;score\u0026nbsp;(USG)\u0026nbsp;and cervical length\u0026nbsp;as a whole method\u0026nbsp;can be employed to predict the probability of cesarean delivery during induction[18-19].\u003c/p\u003e\n\u003cp\u003eDinoprostone is widely approved for cervical ripening before labor induction.Familiari A\u0026nbsp;[20]found that the dinoprostone group had a cesarean delivery rate of 18.1% (95% CI 9.9-28.3) due to induction failure, while the misoprostol group had a rate of 9.4% (95% CI 1.4-22.0), and the mechanical method group had a rate of 8.1% (95% CI 5.0-11.6)\u0026nbsp;in SGA patients..Di Mascio D\u0026nbsp;[21]demonstrated that\u0026nbsp;the rate of cesarean birth (25.6% vs. 17.2%; p = 0.027), composite negative neonatal outcome (26.1% vs. 16.7%; p = 0.013) and NICU admission (16.9% vs. 5.6%; p \u0026lt; 0.001) was greater in\u0026nbsp;pregnancies affected by late fetal growth restriction undergoing IOL with dinoprostone than those performed with mechanical methods.Our findings revealed that dinoprostone doubled the risk of cesarean\u0026nbsp;section\u0026nbsp;(aOR 2.08, 95% CI: 1.13-3.81) compared to the Cook double balloon method. This elevated risk may be attributed to the potential for uterine hyperstimulation following dinoprostone administration[22], particularly in SGA cases complicated by placental insufficiency, necessitating a prompt cesarean delivery.In our research, 318 patients chose cook doble balloon as method of labor induction with safety ,only 189 pregnancies chose dionprostone.Therefore,the safety aspect of dinoprostone usage in inducing labor in SGA pregnancies continues to be a matter of concern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and Research Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nomogram established in this study serve as an invaluable clinical tool for individual risk\u0026nbsp;assessment\u0026nbsp;in pregnancies affected by SGA. By integrating key predictive factors—including maternal age, admission weight, dinoprostone usage, and the\u0026nbsp;Bishop score\u0026nbsp;after cervical ripein—healthcare providers can\u0026nbsp;personalize\u0026nbsp;management strategies for SGA pregnancies. Early identification of high-risk patients empowers individuals to make informed decisions regarding their delivery plans and fosters shared decision-making between clinicians and patients. By presenting risk probabilities visually, the nomogram encourages expectant mothers to actively engage in discussions around their delivery options, potentially enhancing patient satisfaction and compliance with medical recommendations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, the nomogram's ability to accurately determine patients at risk for cesarean delivery facilitates improved resource allocation within healthcare systems. Hospitals can make necessary preparations in advance, ensuring adequate surgical and neonatal care resources are available when needed. Such proactive measures not only enhance patient safety but also optimize operational efficiency within obstetric care facilities.\u0026nbsp;Noneetheless,it’s important to realize that the nomogram is designed to assist in patients consultation,not to directly make clinical decisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Strengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;research has numerous significant advantages such as: (1) a\u0026nbsp;fairly substantial\u0026nbsp;sample size, (2) random\u0026nbsp;division\u0026nbsp;of the\u0026nbsp;group\u0026nbsp;into model\u0026nbsp;creation\u0026nbsp;and validation groups, and (3) the inclusion of easily measurable antepartum variables in the nomogram, making it practical for daily clinical use.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, There are several limitations to our study. As a retrospective, single-center investigation, there may be biases impacting the results. The findings may lack general applicability, as the specific characteristics of our patient population and institutional practices may not be reflective of broader hospital settings. Consequently, the identified risk factors may differ in varied contexts, necessitating validation in larger, multi-center cohorts. Additionally, while we examined key predictors, numerous other factors influencing labor outcomes in SGA pregnancies were not accounted for, such as psychological aspects, socioeconomic status, prenatal care differences, and genetic factors. The exclusion of these variables may limit the comprehensiveness of our predictive model. Finally, further external validation of the nomogram is necessary before widespread implementation in clinical practice. Future research should prioritize multi-center prospective studies to validate our findings and explore the nomogram's applicability in diverse clinical settings.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study validates the predictive capabilities of identified factors for cesarean delivery risk following induction failure in SGA patients through the development of a nomogram. This tool enhances the identification of high-risk patients and supports individualized management. Subsequent investigations should focus on optimizing the utilization of these risk factors to enhance clinical outcomes, reduce cesarean delivery rates, and promote maternal and neonatal well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors helped to perform the research;\u0026nbsp;Mingxing Yan: project development, data analysis, manuscript\u0026nbsp;writing;\u0026nbsp;Liping Hu:\u0026nbsp;projecti development,data collection;Liying Li:\u0026nbsp;Writing-Reviewing and Editing;Mengting Chen:data collection;Jun Shi:Data preparation;\u0026nbsp;Jinji Wang:\u0026nbsp;Investigation;data collection.Feng Li:Writing-Reviewing and Editing.All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there was no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInnovation Platform Project of Science and Technology,Fujian province（2021Y2012）\u003c/p\u003e\n\u003cp\u003eNational Key Clinical Specialty Construction Program of China（Obstetric）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adheres to the principles of the Declaration of Helsinki (2000) of the World Medical Association. Ethics approval was obtained from the Ethics Review Committee of Fujian Maternal and Child Health Hospital [2023KY016]. As this is a retrospective study, the Institutional Review Board waived the need for individual written informed consent from the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided signed informed consent for labor induction. Written informed consent for study participation was obtained from all women included in the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized or analyzed during the current study are accessible from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWilcox AJ, Cortese M, McConnaughey DR, et al.The limits of small-for-gestational-age as a high-risk category[J]. Eur J Epidemiol, 2021, 36(10): 985-991.\u003c/li\u003e\n \u003cli\u003eCastagno M, Menegon V, Monzani A, et al.. Small-for-gestational-age birth is linked to cardiovascular dysfunction in early childhood[J]. Am Heart J, 2019, 217: 84-93.\u003c/li\u003e\n \u003cli\u003eSacchi C, Marino C, Nosarti C, et al.. Association of intrauterine growth restriction and small for gestational age status with childhood cognitive outcomes: a systematic review and meta-analysis[J]. JAMA Pediatr, 2020, 174(8): 772-781.\u003c/li\u003e\n \u003cli\u003eMcCowan LM, Figueras F, Anderson NH. Evidence-based national guidelines for the management of suspected fetal growth restriction:comparison, consensus, and controversy. Am J Obstet Gynecol. 2018 Feb; 218(2 2S):S855\u0026ndash;68.\u003c/li\u003e\n \u003cli\u003eParisi S, Monzeglio C, Attini R, et al. Evidence of lower oxygen reserves during labour in the growth restricted human foetus: a retrospective study. BMC Pregnancy Childbirth. 2017;17(1):209. 6.Nwabuobi C, Gowda N, Schmitz J, Wood N, Pargas A, Bagiardi L, Odibo L, Camisasca-Lopina H, Kuznicki M, Sinkey R, et al. Risk factors for Cesarean delivery in pregnancy with small-for-gestational-age fetus undergoing induction of labor. Ultrasound Obstet Gynecol. 2020 Jun;55(6):799-805.\u003c/li\u003e\n \u003cli\u003eKalafat E, Morales-Rosello J, Thilaganathan B, Tahera F, Khalil A. Risk of operative delivery for intrapartum fetal compromise in small-for-gestational-age fetuses at term: an internally validated prediction model. Am J Obstet Gynecol 2018; 218: 134.e131-134.e138.\u003c/li\u003e\n \u003cli\u003eSimeone S, Marchi L, Canarutto R, Pina Rambaldi M, Serena C, Servienti C, Mecacci F.Doppler velocimetry and adverse outcome in labor induction for late IUGR. J Matern Fetal Neonatal Med 2017; 30: 323-328.\u003c/li\u003e\n \u003cli\u003eKhalil A, Morales-Rosello J, Khan N, Nath M, Agarwal P, Bhide A, Papageorghiou A,\u003c/li\u003e\n \u003cli\u003eThilaganathan B. Is cerebroplacental ratio a marker of impaired fetal growth velocity and adverse pregnancy outcome? Am J Obstet Gynecol 2017; 216: 606.e601-606.e610.\u003c/li\u003e\n \u003cli\u003eRossi RM, Requarth E, Warshak CR, Dufendach KR, Hall ES, DeFranco EA. Risk calculator to predict cesarean delivery among women undergoing induction of labor. Obstet Gynecol. 2020;135(3):559\u0026ndash;68.\u003c/li\u003e\n \u003cli\u003ePinton A, Lemaire Tomzack C, Merckelbagh H, Goffinet F. Induction of labour with unfavourable local conditions for suspected fetal growth restriction after 36 weeks of gestation: Factors associated with the risk of caesarean. J Gynecol Obstet Hum Reprod. 2021 Sep;50(7):101996.\u003c/li\u003e\n \u003cli\u003eAttali E, Doleeb Z, Hiersch L, Amikam U, Gamzu R, Yogev Y, Ashwal E. The risk of intrapartum cesarean delivery in advanced maternal age. J Matern Fetal Neonatal Med. 2022 Dec;35(25):8019-8026.\u003c/li\u003e\n \u003cli\u003eLean SC, Derricott H, Jones RL, Heazell AEP. Advanced maternal age and adverse pregnancy outcomes: A systematic review and meta-analysis. PLoS One 2017; 12:e0186287.\u003c/li\u003e\n \u003cli\u003eQuach D, Ten Eikelder M, Jozwiak M, Davies-Tuck M, Bloemenkamp KWM, Mol BW, Li W. Maternal and fetal characteristics for predicting risk of Cesarean section following induction of labor: pooled analysis of PROBAAT trials. Ultrasound Obstet Gynecol. 2022 Jan;59(1):83-92.\u003c/li\u003e\n \u003cli\u003eNkoka O, Ntenda PAM, Senghore T, Bass P. Maternal overweight and obesity and the risk of caesarean birth in Malawi. Reprod Health. 2019 Apr 3;16(1):40.\u003c/li\u003e\n \u003cli\u003eWinner RM, Graves J, Jarvis K, Beckman D, Youkilis BB, Monroe M, Davies CC. Relationships Among Mode of Birth, Onset of Labor, and Bishop Score. J Obstet Gynecol Neonatal Nurs. 2024 May 20:S0884-2175(24)00069-8.\u003c/li\u003e\n \u003cli\u003eLee DS, Tandel MD, Kwan L, Francoeur AA, Duong HL, Negi M. Favorable Simplified Bishop Score after cervical ripening associated with decreased cesarean birth rate. Am J Obstet Gynecol 2022;4(2):100534.\u003c/li\u003e\n \u003cli\u003eSinha P, Gupta M, Meena S. Comparing Transvaginal Ultrasound Measurements of Cervical Length to Bishop Score in Predicting Cesarean Section Following Induction of Labor: A Prospective Observational Study. Cureus. 2024 Feb 16;16(2):e54335.\u003c/li\u003e\n \u003cli\u003eManchu M, Redla V. Prediction of mode of delivery by an ultrasound score similar to Bishop score and performance of independent predictors. J Ultrasound. 2023 Sep;26(3):619-626.\u003c/li\u003e\n \u003cli\u003eFamiliari A, Khalil A, Rizzo G, Odibo A, Vergani P, Buca D, Hidaka N, Di Mascio D, Nwabuobi C, Simeone S, et al. Adverse intrapartum outcome in pregnancies complicated by small for gestational age and late fetal growth restriction undergoing induction of labor with Dinoprostone, Misoprostol or mechanical methods: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2020 Sep;252:455-467.\u003c/li\u003e\n \u003cli\u003eDi Mascio D, Villalain C, Rizzo G, Morales‐Rosello J, Sileo FG, Maruotti GM, Prefumo F, Galindo A, D\u0026apos;Antonio F; induCtion of labOr in Late fetaL Growth rEstriction (COLLEGE) Study Group. Maternal and neonatal outcomes of pregnancies complicated by late fetal growth restriction undergoing induction of labor with dinoprostone compared with cervical balloon: A retrospective, international study. Acta Obstet Gynecol Scand. 2021 Jul;100(7):1313-1321.\u003c/li\u003e\n \u003cli\u003eShirley M. Dinoprostone vaginal insert: a review in cervical ripening. Drugs.2018;78(15):1615\u0026ndash;1624.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Small for Gestational Age (SGA), Cesarean Delivery, Labor Induction, Risk Factors, Predictive Model","lastPublishedDoi":"10.21203/rs.3.rs-4892379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4892379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to identify antenatal and intrapartum risk factors associated with cesarean delivery in term singleton pregnancies complicated by small for gestational age (SGA) and to develop a predictive model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eWe conducted a retrospective case-control study of 507 SGA patients who underwent labor induction between 2017 and 2022 at Fujian Maternity and Child Health Hospital.Comprehensive data on maternal demographics, obstetric complications, labor induction methods, and neonatal outcomes were collected. 354 (70%) experiencing SGA complications enrolled as the derivation cohort and 153 (30%) included in the validation set. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for cesarean delivery, and a predictive nomogram was developed based on these factors in the derivation cohort,and verified in the validation set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 134 (26.43%) women in the cohort underwent cesarean delivery following labor induction. Four significant independent risk factors for cesarean delivery were identified: maternal age(aOR1.08, 95%CI 1.01-1.15) , weightat admission (aOR 1.04, 95% CI 1.01 - 1.07), the use of dinoprostone for induction(aOR 2.08, 95% CI 1.13-3.81), and the Bishop score after cervical ripening(aOR0.65, 95% CI:0.54-0.80). The constructed nomogram displayed a discriminative ability with an area under the curve (AUC) of 0.78 in the training cohort and 0.77 in the validation cohort. Calibration curves indicated strong agreement（P＞0.05）between predicted probabilities and observed outcomes, while decision curve analysis confirmed significant net benefits across various various threshold probabilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThe developed nomogram provides clinicians with a reliable tool for predicting the likelihood of cesarean delivery in SGA pregnancies undergoing labor induction, aiding in informed decision-making and potentially optimizing clinical management strategies to improve perinatal outcomes.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 13:10:11","doi":"10.21203/rs.3.rs-4892379/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1d88ac5-4809-4d5e-bd45-34420e36fd79","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T23:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-10 13:10:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4892379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4892379","identity":"rs-4892379","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}