The Impact of Cervical Cerclage on Live Birth Outcomes in Cervical Incompetence: A Real-World Study

The Impact of Cervical Cerclage on Live Birth Outcomes in Cervical Incompetence: A Real-World Study | 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 The Impact of Cervical Cerclage on Live Birth Outcomes in Cervical Incompetence: A Real-World Study Xue Li, juanjuan liu, jiahan xu, hong zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8704478/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective To investigate the effect of cervical cerclage on pregnancy outcomes in cervical incompetence using stabilized inverse probability of treatment weighting and multivariable logistic regression. Method A retrospective analysis was conducted of 756 patients with cervical incompetence who attended our Department of Obstetrics and Gynecology between January 2013 and December 2023. Among these, 243 patients underwent expectant management, while 513 underwent cervical cerclage surgery. Stabilized inverse probability of treatment weighting(sIPTW) was employed to balance multiple confounding variables, including parity, number of previous births, number of late miscarriages, prior history of cerclage, and cervical length. Differences in pregnancy outcomes between the two groups were compared before and after weighting, including miscarriage, preterm birth, live birth. The impact of cervical cerclage on live birth outcomes was explored using multivariate logistic regression. Results Prior to sIPTW, significant differences existed between the cervical cerclage group and the expectant management group in terms of gestational age, parity, number of late miscarriages, cervical length, and BMI ( P < 0.05). After sIPTW, no statistically significant differences were observed between the two groups in clinical data. Both before and after sIPTW, the cervical cerclage group had higher rates of term delivery and preterm delivery than the expectant group, while the miscarriage rate was lower. The live birth rate in the cerclage group was significantly higher than that in the expectant group ( P < 0.05). Multivariate logistic regression analysis demonstrated that cervical cerclage showed a positive correlation with live birth outcomes both before and after sIPTW. Subgroup analysis revealed that the efficacy of cervical cerclage varied among different examination groups (interaction term P < 0.05). Conclusion Cervical cerclage significantly improves the rates of full-term delivery and live birth among patients with cervical incompetence while reducing the miscarriage rate, particularly in those with abnormal cervical examination. Cervical cerclage Stabilized inverse probability of treatment weighting Live birth Figures Figure 1 Introduction Cervical incompetence (CIC), also known as cervical insufficiency, refers to a disorder of pregnancy maintenance caused by abnormal cervical anatomy or function during pregnancy in the absence of uterine contractions. Clinically, it manifests as painless cervical dilation and irreversible pregnancy loss [ 1 ] .Epidemiological studies indicate that CIC has an incidence rate of 0.1%–2%, accounting for approximately 8% of late miscarriages and preterm births, making it a significant pathological factor contributing to adverse perinatal outcomes [ 2 ] .Despite widespread recognition of its clinical hazards, the pathological mechanisms underlying this condition remain incompletely elucidated. From an etiological perspective, CIC can be categorized into congenital and acquired forms. Congenital CIC is associated with genetic factors such as cervical developmental abnormalities (e.g., cervical agenesis, cervical hypoplasia) and connective tissue disorders. Acquired CIC results from iatrogenic cervical trauma (e.g., cervical conization, repeated intrauterine procedures) or obstetric injury [ 3 , 4 ] 。Regarding diagnostic criteria, no unified gold standard has been established by international authoritative bodies. Current consensus diagnostic criteria encompass the following three key points: ① History of painless cervical dilation after excluding other causes; ② Cervical length (CL) <25mm detected by transvaginal ultrasound (TVU) before 24 weeks of singleton pregnancy; ③ Progressive cervical dilation confirmed by vaginal examination or speculum examination during mid-pregnancy [ 5 , 6 ] .However, controversies persist among societies regarding specific diagnostic thresholds and timing of assessment, necessitating multicenter clinical studies to establish an evidence-based diagnostic system. Cervical cerclage, as the primary intervention for CIC, has been extensively studied regarding its clinical value and limitations. Current evidence indicates that for pregnant women with clear high-risk factors for CIC, such as a history of mid-term pregnancy loss, cervical shortening, or iatrogenic cervical injury, prophylactic cerclage significantly prolongs gestational age, reduces preterm birth rates before 34 weeks, and improves perinatal survival rates [ 7 , 8 ] .However, this procedure carries surgical risks, including premature rupture of membranes, chorioamnionitis, and cervical laceration. Its efficacy remains controversial for singleton pregnancies without typical medical history but with ultrasound-detected cervical shortening (CL < 25 mm), with some randomized controlled trials (RCTs) showing no significant benefit [ 9 ] .Notably, ACOG emphasizes strict adherence to indications for cerclage: it recommends prophylactic cerclage for patients with ≥ 3 prior mid-term miscarriages/preterm births or those who underwent cervical conization[10], For ultrasound-indicated cases, it suggests stratified management incorporating fetal fibronectin (fFN) testing. Treatment for cervical incompetence (CIC) remains controversial. For instance, studies show no statistically significant difference in late miscarriage or preterm birth rates between observation and surgical groups for women without late miscarriage history who develop progressive cervical shortening or cervical os dilation during pregnancy [ 11 ] .Similarly, no definitive conclusions exist regarding the benefits of cervical cerclage for women with a history of cervical LEEP procedures, polycystic ovary syndrome (PCOS), or those achieving pregnancy through in vitro fertilization (IVF) [ 12 ] .Clinicians consistently aim to avoid both overtreatment and missing the optimal window for clinical intervention in CIC. Overall, the clinical benefits of cervical cerclage exhibit population heterogeneity, necessitating precision interventions based on individualized risk assessment. This study employed a retrospective cohort enrolling patients diagnosed with cervical incompetence at our center. Participants were divided into an expectant management group and cervical cerclage group based on treatment modality. The study aimed to systematically analyze the impact of cervical cerclage on pregnancy outcomes, focusing on improvements in miscarriage rate, preterm birth rate, and live birth rate. To control for confounding factors, robust inverse probability weighting was employed, and conditional logistic regression was used to explore key determinants influencing pregnancy outcomes. Materials and Methods Study Population This retrospective study included patients with cervical incompetence who visited the Second Affiliated Hospital of Soochow University from January 2013 to December 2023. Inclusion criteria: (1) Age ≥ 18 years; (2) Singleton pregnancy; (3) History of one or more late miscarriages; (4) Cervical length ≤ 25 mm detected by transvaginal ultrasound before 24 weeks of gestation. (5) Cervical dilation detected during mid-pregnancy examination. On the basis of meeting the first two criteria, patients who meet any one of (3), (4), or (5) can be enrolled. Exclusion criteria: (1) Fetal intrauterine death or fetal malformations; (2) Multiple gestation; (3) Incomplete clinical data; (4) Uterine malformations, including septate uterus, bicornuate uterus, unicornuate uterus, etc. (5) Prenatal infection. (6) Severe concomitant medical or surgical complications. This study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Soochow University (JD-HG-2025-137). Participants voluntarily enrolled and signed informed consent forms( Fig. 1 ) . Collection of Medical History Data General clinical data includes the age, pre-pregnancy body mass index (BMI), number of pregnancies, number of deliveries, history of late miscarriages, history of cervical cerclage, history of cervical loop electrosurgical excision procedure (LEEP), history of polycystic ovary syndrome (PCOS), and method of conception (spontaneous conception/assisted reproductive technology). Cervical Examination Indicators: Record cervical texture, anterior and posterior vaginal fornix morphology, cervical lacerations, cervical polyps, and length of the cervical vaginal segment. Any occurrence of one or more of the following is defined as an abnormal cervical examination: soft cervical texture, disappearance of the anterior or posterior vaginal fornix, cervical lacerations, cervical polyps, or short cervical vaginal segment. Transvaginal Ultrasound Measurement Parameters: Perform examination after bladder emptying. Place the sterile transvaginal probe in the anterior vaginal fornix, avoiding excessive force. Standard sagittal plane: The cervical image should occupy more than 75% of the screen. Both the external os and the entire cervical canal should be measured. Measure the straight-line distance from the internal os to the external os. Take three consecutive measurements and record the shortest value. The procedure should be performed by the same sonographer. Measured parameters include cervical length and internal os width. Vaginal Secretion Collection and Testing Results: These include routine vaginal discharge tests and vaginal secretion culture results. Specimens were collected preoperatively in the cerclage group and immediately sent for culture identification. Vaginal secretion collection method: After cleaning the external genitalia, the vagina and cervix were exposed using a sterile speculum. A sterile swab was used to collect secretions from the posterior vaginal fornix, promptly placed into a sterile test tube, and immediately sent to the laboratory for testing. Collected specimens underwent routine vaginal discharge analysis and vaginal secretion culture. Patients diagnosed with BV or VVC were excluded from this study. BV diagnosis employed the Gram-stained Nugent scoring system: total score of 10 points, where 0–3 points indicate normal, 4–6 points indicate intermediate BV (transitional state), and ≥ 7 points confirm BV diagnosis. VVC diagnostic criteria: After processing secretions via Gram staining smear, 10% potassium hydroxide wet mount, or saline wet mount, microscopic observation reveals fungal budding spores or pseudohyphae. Vaginal Secretion Culture: Collected specimens were inoculated onto various agar media. These included aerobic Columbia blood agar (Lot No. 20091206B; Zhengzhou Aobo Diagnostics Co., Ltd.), gonococcal culture medium (Lot No. 090919; Shanghai Comag Microbial Technology Co., Ltd.), and chocolate agar (Lot No. 20091221B; Autobio Diagnostics Co., Ltd.). Anaerobic cultures were performed on Columbia blood agar and Candida agar. Pregnancy Outcomes Primary Outcome Measures: Miscarriage rate, preterm birth rate, full-term birth rate, live birth rate. Secondary Outcome Measures: Gestational age at delivery, mode of delivery, pregnancy complications, neonatal birth weight. Pregnancy complications include gestational diabetes, gestational hypertension, intrahepatic cholestasis of pregnancy, intrauterine growth restriction, and preterm premature rupture of membranes. Grouping Criteria Patients were divided into two groups based on treatment modality, Expectant Management Group: Patients diagnosed with CIC who were ineligible for surgery or unable to undergo surgery received expectant management, including bed rest and administration of tocolytics or progesterone. Cerclage group : McDonald procedure. Based on vaginal discharge culture results, patients were divided into two groups, Microbial Dysbiosis Group: Vaginal discharge culture detected any one or multiple pathogenic bacteria listed above, with absence or significant reduction of lactobacilli. Normal Microflora Group: Lactobacilli were the predominant bacteria, with no pathogenic bacteria detected. Based on pregnancy outcome, patients were further categorized into live birth group and non-live birth group. Statistical Analysis This study employed SPSS 26.0 and R software (version 3.6.3), utilizing the stabilized inverse probability of treatment weighting(sIPTW) to balance baseline data between the two groups. The intergroup P-value and standardized mean difference (SMD) were calculated before and after balancing. An SMD < 0.1 was considered indicative of balance. For normally distributed quantitative data, results were expressed as mean ± standard deviation and compared using independent samples t-tests. Non-normally distributed data were presented as median (interquartile range) [M (QL, QU)] and compared using the Mann-Whitney U test. Count data were expressed as cases (rates) and analyzed using chi-square tests. Univariate logistic regression was performed using R software to identify factors influencing live birth. Multivariate logistic regression models were employed to assess associations between cervical cerclage and live birth outcomes via subgroup analysis using forest plots. P < 0.05 was considered statistically significant. Result Comparison of Baseline Characteristics Between the Cervical Cerclage Group and the Expectant Group Based on the inclusion and exclusion criteria, a total of 798 patients were screened in this study, of which 756 met the criteria and were included. Patients were divided into a cervical cerclage group (n = 513) and an expectant management group (n = 243) whether cervical cerclage was performed. Comparison of the clinical baseline data between the cervical cerclage group and the expectant management group before sIPTW revealed statistically significant differences in BMI, previous history of cervical cerclage, parity, previous history of late abortion, and cervical length (P 0.05). (Table 1 )。 Table 1 Comparison of Pregnancy Outcomes Before and After IPTWs Between Cervical Cerclage and Expectant Groups level Before sIPTWs After IPTWs expectant group cervical cerclage group P SMD expectant group cervical cerclage group P SMD (n = 243) (n = 513) (n = 257.82) (n = 515.79) Age ≤ 35 221 (90.9) 456 (88.9) 0.461 0.068 232.7 (90.3) 450.2 (87.3) 0.390 0.095 >35 22 ( 9.1) 57 (11.1) 25.1 ( 9.7) 65.6 (12.7) BMI (kg/m²)) 22.89 [21.34, 25.15] 22.03 [20.31, 23.83] < 0.001 0.385 22.72 [20.83, 24.61] 22.19 [20.45, 24.61] 0.525 0.056 Gravidity 0–2 161 (66.3) 197 (38.4) < 0.001 0.589 110.8 (43.0) 238.3 (46.2) 0.143 0.239 3 60 (24.7) 210 (40.9) 120.5 (46.7) 190.9 (37.0) ≥ 4 22 ( 9.1) 106 (20.7) 26.5 (10.3) 86.6 (16.8) parity 0 225 (92.6) 458 (89.3) 0.191 0.116 241.6 (93.7) 468.1 (90.8) 0.221 0.110 ≥ 1 18 ( 7.4) 55 (10.7) 16.2 ( 6.3) 47.7 ( 9.2) Number of late miscarriage 0 44 (18.1) 27 ( 5.3) < 0.001 0.729 24.1 ( 9.4) 55.4 (10.7) 0.775 0.081 1 164 (67.5) 265 (51.7) 137.2 (53.2) 286.3 (55.5) ≥ 2 35 (14.4) 221 (43.1) 96.4 (37.4) 174.1 (33.8) Prior history of cerclage No 241 (99.2) 461 (89.9) < 0.001 0.418 230.9 (89.6) 478.9 (92.9) 0.595 0.117 Yes 2 ( 0.8) 52 (10.1) 26.9 (10.4) 36.8 ( 7.1) Methods of pregnancy natural pregnancy 223 (91.8) 461 (89.9) 0.483 0.066 239.0 (92.7) 466.6 (90.5) 0.366 0.081 IVF 20 ( 8.2) 52 (10.1) 18.8 ( 7.3) 49.2 ( 9.5) History of leep No 229 (94.2) 492 (95.9) 0.404 0.077 248.1 (96.2) 484.0 (93.8) 0.284 0.110 Yes 14 ( 5.8) 21 ( 4.1) 9.7 ( 3.8) 31.8 ( 6.2) History of pcos No 236 (97.1) 508 (99.0) 0.100 0.139 253.1 (98.2) 500.6 (97.1) 0.554 0.072 Yes 7 ( 2.9) 5 ( 1.0) 4.7 ( 1.8) 15.1 ( 2.9) cervical examination Normal 176 (72.4) 319 (62.2) 0.007 0.220 144.6 (56.1) 333.8 (64.7) 0.219 0.177 Abmormal 67 (27.6) 194 (37.8) 113.3 (43.9) 182.0 (35.3) Cervical length(mm) 30 124 (51.0) 267 (52.0) 140.7 (54.6) 256.6 (49.7) Dilation of cervical os No 200 (82.3) 372 (72.5) 0.005 0.236 187.0 (72.5) 379.5 (73.6) 0.877 0.023 Yes 43 (17.7) 141 (27.5) 70.8 (27.5) 136.3 (26.4) vaginal microbiota Normal 146 (60.1) 394 (76.8) < 0.001 0.366 194.5 (75.4) 372.8 (72.3) 0.478 0.072 dysbiosis 97 (39.9) 119 (23.2) 63.3 (24.6) 143.0 (27.7) IVF: In Vitro Fertilization Comparison of Pregnancy Outcomes Before and After sIPTW Between Cervical Cerclage and Expectant Groups In the cervical cerclage group, there were 41 miscarriages before sIPTW, with 8 surviving fetuses; 92 preterm births; 380 term deliveries; and 480 live births. In the expectant management group, there were 43 miscarriages before matching, with 0 surviving fetuses; 42 preterm births; 158 term deliveries; and 200 live births. After sIPTW, the cervical cerclage group demonstrated significantly higher rates of term delivery and live birth compared to the expectant management group, along with a higher cesarean section rate and lower miscarriage rate (P 0.05)(Table 2 ). Table 2 Comparison of Pregnancy Outcomes Before and After sIPTW Between the Cervical Cerclage Group and the Expectant Group. Pregnancy outcomes Befores sIPTW After sIPTW expectant group(n = 243) cervical cerclage group(n = 513) p expectant group(n = 257.8) cervical cerclage group(n = 515.8) p Term birth 158 (65.0) 380 (74.1) 0.012 155.5 (60.3) 374.6 (72.6) 0.011 Miscarriage 43 (17.7) 41 ( 8.0) <0.001 39.6 (15.3) 38.7 ( 7.5) 0.0003 Preterm birth 42 (17.3) 92 (17.9) 0.827 62.8 (24.3) 102.5 (19.9) 0.079 Live birth 200 (82.3) 480 (93.6) < 0.001 218.3 (84.7) 483.4 (93.7) 0.003 Complications during pregnancy 18 (7.4) 59 (11.5) 0.108 17.7 ( 6.9) 63.7 (12.4) 0.065 pregnancy complications 187 (93.5) 397 (82.7) <0.001 205.9 (94.3) 398.0 (82.3) <0.001 cesarean section 13 (6.5) 83 (17.3) 12.3 (5.7) 85.4 (17.7) Birth weight(g) 3096.93 ± 501.92) 3062.99 ± 627.84 0.497 3029.11 ± 507.46 3066.59 ± 605.76 0.656 gestational age at delivery 37.59 ± 1.88 37.25 ± 2.64 0.098 37.44 ± 1.76 37.24 ± 2.52 0.34 Logistic Regression Models for Live Birth Outcomes Before and After sIPTW Cervical cerclage demonstrated a positive correlation with live birth outcomes in both the pre- and post-sIPTW datasets, with the OR value of the post-sIPTW model being lower than that of the pre-adjusted model. The research results remained consistent after adjusting for various covariates(Table 3 ). Table 3 Logistic Regression Models Under Different Adjustment Factors Before and After sIPTW. Model* Adjusted logistic model sIPTW-adjusted logistic model OR(95%CI) P OR(95%CI) P Model 1 3.127(1.935–5.097) <0.001 1.095(1.016–1.181) 0.018 Model 2 3.266(1.777–6.061) <0.001 1.088(1.017–1.164) 0.015 Model 3 3.192(1.687-6.100) <0.001 1.086(1.021–1.154) 0.009 Model 1: Unadjusted logistic regression model with the expectant management group as reference; Model 2 adjusted for cervical length, number of late miscarriages, cervical examination, cervical dilation, and microbial characteristics; Model 3 adjusted for BMI, age, cervical length, parity, number of births, history of cerclage, number of late miscarriages, cervical examination, method of conception, history of LEEP, history of PCOS, cervical dilation, and microbial characteristics. Subgroup Analysis of the Association Between Cervical Cerclage and Live Birth Outcomes Cervical cerclage efficacy varies across different examination populations (interaction term P < 0.05), with superior outcomes observed in patients with abnormal findings compared to those with normal findings. Cervical cerclage also demonstrates certain variations among women with different parity levels (interaction term P < 0.1). However, further investigation with a larger sample size is required. Cervical cerclage showed no differences across patients with varying parity, number of previous deliveries, number of late miscarriages, cervical length, microbiome status, or method of pregnancy(Table 4). Table 4. Subgroup Analysis of the Association Between Cervical Cerclage and Live Birth Discussion Currently, CIC lacks an internationally recognized standardized diagnostic system. Although major international guidelines converge in principle, there are still significant differences in specific clinical pathways, especially in screening strategies for asymptomatic low-risk populations [ 2 , 10 ] .Transvaginal ultrasound measurement of cervical length holds predictive value in high-risk populations, but most international guidelines do not recommend routine dynamic monitoring for low-risk individuals. China's 2024 Guidelines for the Diagnosis and Management of Preterm Labor adopt a stratified management approach, recommending referral for standardized transvaginal ultrasound assessment when abdominal ultrasound screening reveals a cervical length < 36 mm. This inconsistency in screening criteria directly impacts the timing of early identification and intervention, potentially causing some at-risk individuals to miss the management window [ 13 ] . Cervical cerclage, as the primary treatment for cervical incompetence (CIC), remains controversial regarding its beneficiary population. Currently, for women at high risk of preterm birth (PTB) due to premature cervical ripening or risk of painless cervical dilation, key clinical interventions include progestogen therapy, cerclage, and cervical pessary placement [ 14 ] .The 2021 FIGO practice recommendations for preterm birth prevention suggest that for singleton pregnancies with high risk of preterm birth, such as a history of previous spontaneous preterm birth and/or ultrasound findings indicating cervical shortening, daily vaginal progesterone or weekly 17-hydroxyprogesterone caproate treatment can be used. However, the efficacy of progestogen in preventing preterm birth remains controversial. Its mechanism primarily involves binding to progesterone receptors to inhibit uterine contractions and certain inflammatory pathways, but its anti-inflammatory effect is limited, and the regulatory mechanism of extracellular matrix remodeling in cervical cells has not been fully elucidated. This may be an important reason for its poor efficacy in some populations [ 15 , 16 ] .In addition to pharmacological intervention, cervical cerclage, as a primary method for mechanically strengthening cervical support, has been recommended by consensus in guidelines from multiple countries. Although multiple randomized controlled trials and retrospective studies have confirmed that cerclage can improve pregnancy outcomes for some patients with cervical incompetence, its efficacy still exhibits significant population heterogeneity. Currently, it is not fully understood why this procedure is only significantly effective for some patients. On the other hand, cervical pessaries, as a non-invasive intervention, have shown potential in preventing preterm birth in some studies, but recent large multicenter randomized trials have not confirmed their universal clinical benefits [ 17 ] . Against this backdrop, our study utilized real-world data and employed sIPTW to effectively control for confounding factors, thereby balancing key baseline differences between the cervical cerclage group and the expectant management group. This approach enabled an assessment of the independent effect of cervical cerclage on pregnancy outcomes under near-randomized conditions. Our findings confirm that cervical cerclage significantly improves live birth and term delivery rates in patients with cervical incompetence while reducing miscarriage risk. This protective effect is independent of other confounding factors and yields greater benefits in patients with abnormal cervical examination findings. A key contribution of this study lies in successfully applying sIPTW to address unavoidable selection bias in real-world research. Prior to weighting, patients opting for cerclage typically exhibited more unfavorable baseline characteristics, such as a higher number of late miscarriages and shorter cervical length which accurately reflects clinical practice where physicians preferentially intervene in high-risk cases. Traditional comparisons would severely underestimate the true efficacy of cerclage and could even yield misleading conclusions. sIPTW balances these confounding variables, simulating a near-randomized controlled setting that enables comparability between groups. Analysis revealed cerclage achieved an odds ratio (OR) of 1.089 for live births, highlighting the procedure's independent positive therapeutic effect after controlling for confounders. This study included complex cases often excluded from strict RCTs (e.g., emergency cerclage, multiple prior surgeries), yielding conclusions that better reflect the complexity and diversity of routine clinical practice. The findings align with multiple classic RCTs and meta-analyses, collectively affirming the efficacy of cerclage in specific populations [ 18 ] .This study has certain limitations. Firstly, despite being corrected by sIPTW, its retrospective design still cannot completely eliminate the influence of all unmeasured confounding factors, such as patient compliance. Secondly, the research data comes from a single center, and although the sample size is large, the extrapolation of conclusions still requires multi-center data verification. In addition, a more detailed stratified analysis of the specific content of expectant treatment ,such as whether progesterone is used and the degree of bed rest has not been conducted. This conclusion provides high-level evidence support for clinicians and patients facing decision-making dilemmas in clinical practice. It also suggests that we should advocate for cervical examination as an indispensable component in assessing CIC, combined with ultrasound imaging to establish a more comprehensive preoperative evaluation system. The benefits of cervical cerclage may be subject to both a “time window” and a “population window.” Future research should focus on integrating multidimensional information, such as vaginal microbiome markers, inflammatory cytokines, and cervical elastography to develop predictive models for individualized treatment responses [ 19 , 20 ] , This approach will facilitate a shift from empirical treatment to precision-based, personalized management. Declarations Ethics statements This study was approved by the Ethics Committee of the second affiliated hospital of soochow university (JD-HG-2025-137). The study was conducted according to the ethical principles stated in the Declaration of Helsinki. Informed consent was obtained from all subjects. Consent for publication Not applicable. Availability of Data and Materials The datasets generated during this study cannot be made fully publicly available due to patient privacy restrictions. However, de-identified data may be shared with editors or qualified researchers upon formal request, contingent on obtaining explicit consent from participants and approval from the relevant ethics committee. Funding This project was supported by National Natural Science Foundation of China (82571922) and Jiangsu Provincial Natural Science Foundation BK20241791. Conflict of Interest The authors declare that they have no competing interests. Authors' contributions Xue Li and Hong Zhang conceived and designed the study. Juanjuan Liu performed the experiments and analyzed the data. Jiahan Xu developed the simulation models and prepared all figures. 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D'ANTONIO F, DI MASCIO D BERGHELLAV, et al. Role of progesterone, cerclage and pessary in preventing preterm birth in twin pregnancies: A systematic review and network meta-analysis [J]. Eur J Obstet Gynecol Reprod Biol. 2021;261:166–77. VAN DIJK C E, BREUKING S H, JANSEN S et al. Perioperative complications of a transvaginal cervical cerclage in singleton pregnancies: a systematic review and meta-analysis [J]. Am J Obstet Gynecol, 2022. ONYANGO S, MI J D, KOECH A, et al. Corrigendum: Microbiota dynamics, metabolic and immune interactions in the cervicovaginal environment and their role in spontaneous preterm birth [J]. Front Immunol. 2024;15:1374545. LIU J, XU M, ZHOU L, et al. Early Magnetic Resonance Imaging Measurements and Prediction of Second Trimester Pregnancy Loss: a Nomogram Model Analysis [J]. Int J Womens Health. 2024;16:819–27. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Apr, 2026 Editor assigned by journal 18 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 21 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Submission checks completed at journal 07 Feb, 2026 First submitted to journal 07 Feb, 2026 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. 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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-8704478","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590718614,"identity":"bb4e8c80-5e12-484a-a0c3-669a000466c1","order_by":0,"name":"Xue Li","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Li","suffix":""},{"id":590718616,"identity":"4ded3af1-c6d7-438b-8151-6e69034c4583","order_by":1,"name":"juanjuan liu","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"juanjuan","middleName":"","lastName":"liu","suffix":""},{"id":590718617,"identity":"66936893-af1c-47ef-a04d-1098d4e98edc","order_by":2,"name":"jiahan xu","email":"","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"jiahan","middleName":"","lastName":"xu","suffix":""},{"id":590718618,"identity":"e731829b-d4af-44a1-8b7f-0c07ff6658fc","order_by":3,"name":"hong zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYHACNhCRACIOfKiw4eFnbyBeC+PBGWfSZCR7DhCvhfkwb8thG4MbDvjVy/cff/bg547aPH7p9gsHeBvO8zDcYGD88DEHtxaDAwfSDXvPHC+WnHOm4IDkjts8jLMbmCVnbsOjhbHhmARv27HEDTdyEg4YnrnNwyxzgI2ZF48W+WbGNsm/MC2Jbed42CQS8GthOMbMJs3bVgPUkn7gwMG2Azw8hLQYnGFjk5ZtO5A4c0YOw8GGM8k8EjwHm/H6BRRikm/b6hL7JdIff/5TYWdvf7z54IeP+BwGAYeBmMcAymFsIKgeCOqAmP0BMSpHwSgYBaNgBAIAOs5cqdMsMLIAAAAASUVORK5CYII=","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"hong","middleName":"","lastName":"zhang","suffix":""}],"badges":[],"createdAt":"2026-01-27 01:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8704478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8704478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102941815,"identity":"ffdbcdc3-edbb-4c0e-9ed1-261ca68eefb3","added_by":"auto","created_at":"2026-02-18 17:21:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80269,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8704478/v1/03cb48c498e56d395223f7b7.png"},{"id":102964094,"identity":"ee12455a-e455-4a26-9ffa-7be2410b1f47","added_by":"auto","created_at":"2026-02-19 04:21:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":903782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8704478/v1/b8619696-c49c-438f-9828-6eaa68a21d86.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Cervical Cerclage on Live Birth Outcomes in Cervical Incompetence: A Real-World Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical incompetence (CIC), also known as cervical insufficiency, refers to a disorder of pregnancy maintenance caused by abnormal cervical anatomy or function during pregnancy in the absence of uterine contractions. Clinically, it manifests as painless cervical dilation and irreversible pregnancy loss\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.Epidemiological studies indicate that CIC has an incidence rate of 0.1%\u0026ndash;2%, accounting for approximately 8% of late miscarriages and preterm births, making it a significant pathological factor contributing to adverse perinatal outcomes\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.Despite widespread recognition of its clinical hazards, the pathological mechanisms underlying this condition remain incompletely elucidated.\u003c/p\u003e \u003cp\u003eFrom an etiological perspective, CIC can be categorized into congenital and acquired forms. Congenital CIC is associated with genetic factors such as cervical developmental abnormalities (e.g., cervical agenesis, cervical hypoplasia) and connective tissue disorders. Acquired CIC results from iatrogenic cervical trauma (e.g., cervical conization, repeated intrauterine procedures) or obstetric injury\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e。Regarding diagnostic criteria, no unified gold standard has been established by international authoritative bodies. Current consensus diagnostic criteria encompass the following three key points: ① History of painless cervical dilation after excluding other causes; ② Cervical length (CL) \u0026lt;25mm detected by transvaginal ultrasound (TVU) before 24 weeks of singleton pregnancy; ③ Progressive cervical dilation confirmed by vaginal examination or speculum examination during mid-pregnancy\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.However, controversies persist among societies regarding specific diagnostic thresholds and timing of assessment, necessitating multicenter clinical studies to establish an evidence-based diagnostic system.\u003c/p\u003e \u003cp\u003eCervical cerclage, as the primary intervention for CIC, has been extensively studied regarding its clinical value and limitations. Current evidence indicates that for pregnant women with clear high-risk factors for CIC, such as a history of mid-term pregnancy loss, cervical shortening, or iatrogenic cervical injury, prophylactic cerclage significantly prolongs gestational age, reduces preterm birth rates before 34 weeks, and improves perinatal survival rates\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.However, this procedure carries surgical risks, including premature rupture of membranes, chorioamnionitis, and cervical laceration. Its efficacy remains controversial for singleton pregnancies without typical medical history but with ultrasound-detected cervical shortening (CL\u0026thinsp;\u0026lt;\u0026thinsp;25 mm), with some randomized controlled trials (RCTs) showing no significant benefit\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.Notably, ACOG emphasizes strict adherence to indications for cerclage: it recommends prophylactic cerclage for patients with \u0026ge;\u0026thinsp;3 prior mid-term miscarriages/preterm births or those who underwent cervical conization[10], For ultrasound-indicated cases, it suggests stratified management incorporating fetal fibronectin (fFN) testing. Treatment for cervical incompetence (CIC) remains controversial. For instance, studies show no statistically significant difference in late miscarriage or preterm birth rates between observation and surgical groups for women without late miscarriage history who develop progressive cervical shortening or cervical os dilation during pregnancy\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.Similarly, no definitive conclusions exist regarding the benefits of cervical cerclage for women with a history of cervical LEEP procedures, polycystic ovary syndrome (PCOS), or those achieving pregnancy through in vitro fertilization (IVF)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.Clinicians consistently aim to avoid both overtreatment and missing the optimal window for clinical intervention in CIC. Overall, the clinical benefits of cervical cerclage exhibit population heterogeneity, necessitating precision interventions based on individualized risk assessment.\u003c/p\u003e \u003cp\u003eThis study employed a retrospective cohort enrolling patients diagnosed with cervical incompetence at our center. Participants were divided into an expectant management group and cervical cerclage group based on treatment modality. The study aimed to systematically analyze the impact of cervical cerclage on pregnancy outcomes, focusing on improvements in miscarriage rate, preterm birth rate, and live birth rate. To control for confounding factors, robust inverse probability weighting was employed, and conditional logistic regression was used to explore key determinants influencing pregnancy outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eStudy Population\u003c/p\u003e \u003cp\u003eThis retrospective study included patients with cervical incompetence who visited the Second Affiliated Hospital of Soochow University from January 2013 to December 2023. Inclusion criteria: (1) Age ≥ 18 years; (2) Singleton pregnancy; (3) History of one or more late miscarriages; (4) Cervical length ≤ 25 mm detected by transvaginal ultrasound before 24 weeks of gestation. (5) Cervical dilation detected during mid-pregnancy examination. On the basis of meeting the first two criteria, patients who meet any one of (3), (4), or (5) can be enrolled.\u003c/p\u003e \u003cp\u003eExclusion criteria: (1) Fetal intrauterine death or fetal malformations; (2) Multiple gestation; (3) Incomplete clinical data; (4) Uterine malformations, including septate uterus, bicornuate uterus, unicornuate uterus, etc. (5) Prenatal infection. (6) Severe concomitant medical or surgical complications. This study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Soochow University (JD-HG-2025-137). Participants voluntarily enrolled and signed informed consent forms( Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCollection of Medical History Data\u003c/p\u003e \u003cp\u003eGeneral clinical data includes the age, pre-pregnancy body mass index (BMI), number of pregnancies, number of deliveries, history of late miscarriages, history of cervical cerclage, history of cervical loop electrosurgical excision procedure (LEEP), history of polycystic ovary syndrome (PCOS), and method of conception (spontaneous conception/assisted reproductive technology).\u003c/p\u003e \u003cp\u003eCervical Examination Indicators: Record cervical texture, anterior and posterior vaginal fornix morphology, cervical lacerations, cervical polyps, and length of the cervical vaginal segment. Any occurrence of one or more of the following is defined as an abnormal cervical examination: soft cervical texture, disappearance of the anterior or posterior vaginal fornix, cervical lacerations, cervical polyps, or short cervical vaginal segment.\u003c/p\u003e \u003cp\u003eTransvaginal Ultrasound Measurement Parameters: Perform examination after bladder emptying. Place the sterile transvaginal probe in the anterior vaginal fornix, avoiding excessive force. Standard sagittal plane: The cervical image should occupy more than 75% of the screen. Both the external os and the entire cervical canal should be measured. Measure the straight-line distance from the internal os to the external os. Take three consecutive measurements and record the shortest value. The procedure should be performed by the same sonographer. Measured parameters include cervical length and internal os width.\u003c/p\u003e \u003cp\u003eVaginal Secretion Collection and Testing Results: These include routine vaginal discharge tests and vaginal secretion culture results. Specimens were collected preoperatively in the cerclage group and immediately sent for culture identification. Vaginal secretion collection method: After cleaning the external genitalia, the vagina and cervix were exposed using a sterile speculum. A sterile swab was used to collect secretions from the posterior vaginal fornix, promptly placed into a sterile test tube, and immediately sent to the laboratory for testing. Collected specimens underwent routine vaginal discharge analysis and vaginal secretion culture. Patients diagnosed with BV or VVC were excluded from this study. BV diagnosis employed the Gram-stained Nugent scoring system: total score of 10 points, where 0–3 points indicate normal, 4–6 points indicate intermediate BV (transitional state), and ≥ 7 points confirm BV diagnosis. VVC diagnostic criteria: After processing secretions via Gram staining smear, 10% potassium hydroxide wet mount, or saline wet mount, microscopic observation reveals fungal budding spores or pseudohyphae.\u003c/p\u003e \u003cp\u003eVaginal Secretion Culture: Collected specimens were inoculated onto various agar media. These included aerobic Columbia blood agar (Lot No. 20091206B; Zhengzhou Aobo Diagnostics Co., Ltd.), gonococcal culture medium (Lot No. 090919; Shanghai Comag Microbial Technology Co., Ltd.), and chocolate agar (Lot No. 20091221B; Autobio Diagnostics Co., Ltd.). Anaerobic cultures were performed on Columbia blood agar and Candida agar.\u003c/p\u003e \u003cp\u003ePregnancy Outcomes\u003c/p\u003e \u003cp\u003ePrimary Outcome Measures: Miscarriage rate, preterm birth rate, full-term birth rate, live birth rate.\u003c/p\u003e \u003cp\u003eSecondary Outcome Measures: Gestational age at delivery, mode of delivery, pregnancy complications, neonatal birth weight. Pregnancy complications include gestational diabetes, gestational hypertension, intrahepatic cholestasis of pregnancy, intrauterine growth restriction, and preterm premature rupture of membranes.\u003c/p\u003e \u003cp\u003eGrouping Criteria\u003c/p\u003e \u003cp\u003ePatients were divided into two groups based on treatment modality, Expectant Management Group: Patients diagnosed with CIC who were ineligible for surgery or unable to undergo surgery received expectant management, including bed rest and administration of tocolytics or progesterone. Cerclage group : McDonald procedure.\u003c/p\u003e \u003cp\u003eBased on vaginal discharge culture results, patients were divided into two groups, Microbial Dysbiosis Group: Vaginal discharge culture detected any one or multiple pathogenic bacteria listed above, with absence or significant reduction of lactobacilli. Normal Microflora Group: Lactobacilli were the predominant bacteria, with no pathogenic bacteria detected.\u003c/p\u003e \u003cp\u003eBased on pregnancy outcome, patients were further categorized into live birth group and non-live birth group.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThis study employed SPSS 26.0 and R software (version 3.6.3), utilizing the stabilized inverse probability of treatment weighting(sIPTW) to balance baseline data between the two groups. The intergroup P-value and standardized mean difference (SMD) were calculated before and after balancing. An SMD \u0026lt; 0.1 was considered indicative of balance. For normally distributed quantitative data, results were expressed as mean ± standard deviation and compared using independent samples t-tests. Non-normally distributed data were presented as median (interquartile range) [M (QL, QU)] and compared using the Mann-Whitney U test. Count data were expressed as cases (rates) and analyzed using chi-square tests. Univariate logistic regression was performed using R software to identify factors influencing live birth. Multivariate logistic regression models were employed to assess associations between cervical cerclage and live birth outcomes via subgroup analysis using forest plots. P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eComparison of Baseline Characteristics Between the Cervical Cerclage Group and the Expectant Group\u003c/p\u003e\u003cp\u003eBased on the inclusion and exclusion criteria, a total of 798 patients were screened in this study, of which 756 met the criteria and were included. Patients were divided into a cervical cerclage group (n = 513) and an expectant management group (n = 243) whether cervical cerclage was performed. Comparison of the clinical baseline data between the cervical cerclage group and the expectant management group before sIPTW revealed statistically significant differences in BMI, previous history of cervical cerclage, parity, previous history of late abortion, and cervical length (P \u0026lt; 0.05). To control for confounding factors between groups, sIPTW balancing was performed, after which all differences in baseline data between groups were no longer statistically significant (P \u0026gt; 0.05). (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)。\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Pregnancy Outcomes Before and After IPTWs Between Cervical Cerclage and Expectant Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003c/colgroup\u003e \u003ctbody\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" rowspan=\"3\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\"\u003e \u003cp\u003eBefore sIPTWs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\"\u003e \u003cp\u003eAfter IPTWs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eexpectant group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ecervical cerclage group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eexpectant group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ecervical cerclage group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(n = 243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(n = 513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(n = 257.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e(n = 515.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e221 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e456 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e232.7 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e450.2 (87.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22 ( 9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e57 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.1 ( 9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e65.6 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBMI (kg/m²))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.89 [21.34, 25.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.03 [20.31, 23.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.72 [20.83, 24.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22.19 [20.45, 24.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0–2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e161 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e197 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e110.8 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e238.3 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e60 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e210 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e120.5 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e190.9 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22 ( 9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e106 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e26.5 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e86.6 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eparity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e225 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e458 (89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e241.6 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e468.1 (90.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18 ( 7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e55 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16.2 ( 6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e47.7 ( 9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNumber of late miscarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e44 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e27 ( 5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24.1 ( 9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e55.4 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e164 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e265 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e137.2 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e286.3 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e35 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e221 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e96.4 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e174.1 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePrior history of cerclage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e241 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e461 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e230.9 (89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e478.9 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2 ( 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e52 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e26.9 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e36.8 ( 7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMethods of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003enatural pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e223 (91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e461 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e239.0 (92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e466.6 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20 ( 8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e52 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.8 ( 7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e49.2 ( 9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHistory of leep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e229 (94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e492 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e248.1 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e484.0 (93.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14 ( 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21 ( 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.7 ( 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e31.8 ( 6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHistory of pcos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e236 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e508 (99.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e253.1 (98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e500.6 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7 ( 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5 ( 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.7 ( 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15.1 ( 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ecervical examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e176 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e319 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e144.6 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e333.8 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAbmormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e67 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e194 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e113.3 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e182.0 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCervical length(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e80 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e34.7 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e93.1 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25–30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e78 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e166 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e82.5 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e166.1 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e124 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e267 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e140.7 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e256.6 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDilation of cervical os\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e200 (82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e372 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e187.0 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e379.5 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e141 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e70.8 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e136.3 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003evaginal microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e146 (60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e394 (76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e194.5 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e372.8 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003edysbiosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e97 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e119 (23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e63.3 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e143.0 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eIVF: In Vitro Fertilization\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparison of Pregnancy Outcomes Before and After sIPTW Between Cervical Cerclage and Expectant Groups\u003c/p\u003e\u003cp\u003eIn the cervical cerclage group, there were 41 miscarriages before sIPTW, with 8 surviving fetuses; 92 preterm births; 380 term deliveries; and 480 live births. In the expectant management group, there were 43 miscarriages before matching, with 0 surviving fetuses; 42 preterm births; 158 term deliveries; and 200 live births. After sIPTW, the cervical cerclage group demonstrated significantly higher rates of term delivery and live birth compared to the expectant management group, along with a higher cesarean section rate and lower miscarriage rate (P \u0026lt; 0.05). No significant differences were observed between groups regarding gestational complications, neonatal birth weight, or gestational age at delivery (P \u0026gt; 0.05)(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Pregnancy Outcomes Before and After sIPTW Between the Cervical Cerclage Group and the Expectant Group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003ePregnancy outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003eBefores sIPTW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\"\u003e \u003cp\u003eAfter sIPTW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eexpectant group(n = 243)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ecervical cerclage group(n = 513)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eexpectant group(n = 257.8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ecervical cerclage group(n = 515.8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTerm birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e158 (65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e380 (74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e155.5 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e374.6 (72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMiscarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e43 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e41 ( 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e39.6 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e38.7 ( 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePreterm birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e92 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e62.8 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e102.5 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLive birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e200 (82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e480 (93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e218.3 (84.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e483.4 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eComplications during pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e59 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17.7 ( 6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e63.7 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003epregnancy complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e187 (93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e397 (82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e205.9 (94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e398.0 (82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ecesarean section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e13 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e83 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e12.3 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e85.4 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eBirth weight(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3096.93 ± 501.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3062.99 ± 627.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3029.11 ± 507.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3066.59 ± 605.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003egestational age at delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37.59 ± 1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37.25 ± 2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37.44 ± 1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e37.24 ± 2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLogistic Regression Models for Live Birth Outcomes Before and After sIPTW\u003c/p\u003e\u003cp\u003eCervical cerclage demonstrated a positive correlation with live birth outcomes in both the pre- and post-sIPTW datasets, with the OR value of the post-sIPTW model being lower than that of the pre-adjusted model. The research results remained consistent after adjusting for various covariates(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Models Under Different Adjustment Factors Before and After sIPTW.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eModel*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eAdjusted logistic model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003esIPTW-adjusted logistic model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.127(1.935–5.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.095(1.016–1.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.266(1.777–6.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.088(1.017–1.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.192(1.687-6.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.086(1.021–1.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eModel 1: Unadjusted logistic regression model with the expectant management group as reference; Model 2 adjusted for cervical length, number of late miscarriages, cervical examination, cervical dilation, and microbial characteristics; Model 3 adjusted for BMI, age, cervical length, parity, number of births, history of cerclage, number of late miscarriages, cervical examination, method of conception, history of LEEP, history of PCOS, cervical dilation, and microbial characteristics.\u003c/p\u003e\u003cp\u003eSubgroup Analysis of the Association Between Cervical Cerclage and Live Birth Outcomes\u003c/p\u003e\u003cp\u003eCervical cerclage efficacy varies across different examination populations (interaction term P \u0026lt; 0.05), with superior outcomes observed in patients with abnormal findings compared to those with normal findings. Cervical cerclage also demonstrates certain variations among women with different parity levels (interaction term P \u0026lt; 0.1).\u003c/p\u003e\u003cp\u003eHowever, further investigation with a larger sample size is required. Cervical cerclage showed no differences across patients with varying parity, number of previous deliveries, number of late miscarriages, cervical length, microbiome status, or method of pregnancy(Table\u0026nbsp;4).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;4. Subgroup Analysis of the Association Between Cervical Cerclage and Live Birth\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, CIC lacks an internationally recognized standardized diagnostic system. Although major international guidelines converge in principle, there are still significant differences in specific clinical pathways, especially in screening strategies for asymptomatic low-risk populations \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.Transvaginal ultrasound measurement of cervical length holds predictive value in high-risk populations, but most international guidelines do not recommend routine dynamic monitoring for low-risk individuals. China's 2024 Guidelines for the Diagnosis and Management of Preterm Labor adopt a stratified management approach, recommending referral for standardized transvaginal ultrasound assessment when abdominal ultrasound screening reveals a cervical length\u0026thinsp;\u0026lt;\u0026thinsp;36 mm. This inconsistency in screening criteria directly impacts the timing of early identification and intervention, potentially causing some at-risk individuals to miss the management window\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCervical cerclage, as the primary treatment for cervical incompetence (CIC), remains controversial regarding its beneficiary population. Currently, for women at high risk of preterm birth (PTB) due to premature cervical ripening or risk of painless cervical dilation, key clinical interventions include progestogen therapy, cerclage, and cervical pessary placement\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.The 2021 FIGO practice recommendations for preterm birth prevention suggest that for singleton pregnancies with high risk of preterm birth, such as a history of previous spontaneous preterm birth and/or ultrasound findings indicating cervical shortening, daily vaginal progesterone or weekly 17-hydroxyprogesterone caproate treatment can be used. However, the efficacy of progestogen in preventing preterm birth remains controversial. Its mechanism primarily involves binding to progesterone receptors to inhibit uterine contractions and certain inflammatory pathways, but its anti-inflammatory effect is limited, and the regulatory mechanism of extracellular matrix remodeling in cervical cells has not been fully elucidated. This may be an important reason for its poor efficacy in some populations \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.In addition to pharmacological intervention, cervical cerclage, as a primary method for mechanically strengthening cervical support, has been recommended by consensus in guidelines from multiple countries. Although multiple randomized controlled trials and retrospective studies have confirmed that cerclage can improve pregnancy outcomes for some patients with cervical incompetence, its efficacy still exhibits significant population heterogeneity. Currently, it is not fully understood why this procedure is only significantly effective for some patients. On the other hand, cervical pessaries, as a non-invasive intervention, have shown potential in preventing preterm birth in some studies, but recent large multicenter randomized trials have not confirmed their universal clinical benefits \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, our study utilized real-world data and employed sIPTW to effectively control for confounding factors, thereby balancing key baseline differences between the cervical cerclage group and the expectant management group. This approach enabled an assessment of the independent effect of cervical cerclage on pregnancy outcomes under near-randomized conditions. Our findings confirm that cervical cerclage significantly improves live birth and term delivery rates in patients with cervical incompetence while reducing miscarriage risk. This protective effect is independent of other confounding factors and yields greater benefits in patients with abnormal cervical examination findings. A key contribution of this study lies in successfully applying sIPTW to address unavoidable selection bias in real-world research. Prior to weighting, patients opting for cerclage typically exhibited more unfavorable baseline characteristics, such as a higher number of late miscarriages and shorter cervical length which accurately reflects clinical practice where physicians preferentially intervene in high-risk cases. Traditional comparisons would severely underestimate the true efficacy of cerclage and could even yield misleading conclusions. sIPTW balances these confounding variables, simulating a near-randomized controlled setting that enables comparability between groups. Analysis revealed cerclage achieved an odds ratio (OR) of 1.089 for live births, highlighting the procedure's independent positive therapeutic effect after controlling for confounders. This study included complex cases often excluded from strict RCTs (e.g., emergency cerclage, multiple prior surgeries), yielding conclusions that better reflect the complexity and diversity of routine clinical practice. The findings align with multiple classic RCTs and meta-analyses, collectively affirming the efficacy of cerclage in specific populations\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.This study has certain limitations. Firstly, despite being corrected by sIPTW, its retrospective design still cannot completely eliminate the influence of all unmeasured confounding factors, such as patient compliance. Secondly, the research data comes from a single center, and although the sample size is large, the extrapolation of conclusions still requires multi-center data verification. In addition, a more detailed stratified analysis of the specific content of expectant treatment ,such as whether progesterone is used and the degree of bed rest has not been conducted.\u003c/p\u003e \u003cp\u003eThis conclusion provides high-level evidence support for clinicians and patients facing decision-making dilemmas in clinical practice. It also suggests that we should advocate for cervical examination as an indispensable component in assessing CIC, combined with ultrasound imaging to establish a more comprehensive preoperative evaluation system. The benefits of cervical cerclage may be subject to both a \u0026ldquo;time window\u0026rdquo; and a \u0026ldquo;population window.\u0026rdquo; Future research should focus on integrating multidimensional information, such as vaginal microbiome markers, inflammatory cytokines, and cervical elastography to develop predictive models for individualized treatment responses\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, This approach will facilitate a shift from empirical treatment to precision-based, personalized management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the second affiliated hospital of soochow university (JD-HG-2025-137). The study was conducted according to the ethical principles stated in the Declaration of Helsinki. Informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during this study cannot be made fully publicly available due to patient privacy restrictions. However, de-identified data may be shared with editors or qualified researchers upon formal request, contingent on obtaining explicit consent from participants and approval from the relevant ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by National Natural Science Foundation of China (82571922) and Jiangsu Provincial Natural Science Foundation BK20241791.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXue Li and Hong Zhang conceived and designed the study. Juanjuan Liu performed the experiments and analyzed the data. Jiahan Xu developed the simulation models and prepared all figures. Xue Li wrote the initial manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eALFIREVIC Z, STAMPALIJA T. Cervical stitch (cerclage) for preventing preterm birth in singleton pregnancy [J]. Cochrane Database Syst Rev. 2017;6:CD008991.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHENNAN A, STORY L, JACOBSSON B, et al. FIGO good practice recommendations on cervical cerclage for prevention of preterm birth [J]. Int J Gynaecol Obstet. 2021;155(1):19\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVINK J, MYERS K. Cervical alterations in pregnancy [J]. Best Pract Res Clin Obstet Gynaecol. 2018;52:88\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSTORY L. Cervical cerclage: An evolving evidence base [J]. BJOG: Int J Obstet Gynecol. 2024;131(12):1579\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDUDE A, MILLER ES. Change in Cervical Length across Pregnancies and Preterm Delivery [J]. Am J Perinatol. 2020;37(6):598\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHICKLAND MM, GLAZEWSKA-HALLIN A STORYL, et al. Efficacy of transvaginal cervical cerclage in women at risk of preterm birth following previous emergency cesarean section [J]. Acta Obstet Gynecol Scand. 2020;99(11):1486\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOWEN J, HANKINS G, IAMS JD, et al. Multicenter randomized trial of cerclage for preterm birth prevention in high-risk women with shortened midtrimester cervical length [J]. Am J Obstet Gynecol. 2009;201(4):e3751\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIAO Y, HUANG S, YU W, et al. Effects of emergency/nonemergency cervical cerclage on the vaginal microbiome of pregnant women with cervical incompetence [J]. Front Cell Infect Microbiol. 2023;13:1072960.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMARCHAND G J, MASOUD A T, GALITSKY A et al. Complications of Laparoscopic and Transabdominal Cerclage in Patients with Cervical Insufficiency: A Systematic Review and Meta-analysis [J]. J Minim Invasive Gynecol, 2021, 28(4): 759\u0026thinsp;\u0026ndash;\u0026thinsp;68 e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eACOG Practice Bulletin 142. Cerclage for the management of cervical insufficiency [J]. Obstet Gynecol. 2014;123(2 Pt 1):372\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eENAKPENE C A, DIGIOVANNI L, JONES T N, et al. Cervical cerclage for singleton pregnant patients on vaginal progesterone with progressive cervical shortening [J]. Am J Obstet Gynecol. 2018;219(4):e3971\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYAMAMOTO M, FEIGENBAUM S L, CRITES Y, et al. Risk of preterm delivery in non-diabetic women with polycystic ovarian syndrome [J]. J Perinatol. 2012;32(10):770\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBACHAR G, ATTIA M, FARAGO N, et al. Which part of cervical length is predictive of preterm birth in women with cerclage? [J]. J Matern Fetal Neonatal Med. 2022;35(26):10647\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePERGIALIOTIS V, PSARRIS A, ANTSAKLIS P, et al. Cervical cerclage vs pessary in women with a sonographic short cervix [J]. Ultraschall Med; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePUTORA K, HORNUNG R, KINKEL J, et al. Progesterone, cervical cerclage or cervical pessary to prevent preterm birth: a decision-making analysis of international guidelines [J]. BMC Pregnancy Childbirth. 2022;22(1):355.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSUGITA Y, KUWABARA Y, KATAYAMA A, et al. Characteristic impairment of progesterone response in cultured cervical fibroblasts obtained from patients with refractory cervical insufficiency [J]. Sci Rep. 2023;13(1):11709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'ANTONIO F, DI MASCIO D BERGHELLAV, et al. Role of progesterone, cerclage and pessary in preventing preterm birth in twin pregnancies: A systematic review and network meta-analysis [J]. Eur J Obstet Gynecol Reprod Biol. 2021;261:166\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVAN DIJK C E, BREUKING S H, JANSEN S et al. Perioperative complications of a transvaginal cervical cerclage in singleton pregnancies: a systematic review and meta-analysis [J]. Am J Obstet Gynecol, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eONYANGO S, MI J D, KOECH A, et al. Corrigendum: Microbiota dynamics, metabolic and immune interactions in the cervicovaginal environment and their role in spontaneous preterm birth [J]. Front Immunol. 2024;15:1374545.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIU J, XU M, ZHOU L, et al. Early Magnetic Resonance Imaging Measurements and Prediction of Second Trimester Pregnancy Loss: a Nomogram Model Analysis [J]. Int J Womens Health. 2024;16:819\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cervical cerclage, Stabilized inverse probability of treatment weighting, Live birth","lastPublishedDoi":"10.21203/rs.3.rs-8704478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8704478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the effect of cervical cerclage on pregnancy outcomes in cervical incompetence using stabilized inverse probability of treatment weighting and multivariable logistic regression.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted of 756 patients with cervical incompetence who attended our Department of Obstetrics and Gynecology between January 2013 and December 2023. Among these, 243 patients underwent expectant management, while 513 underwent cervical cerclage surgery. Stabilized inverse probability of treatment weighting(sIPTW) was employed to balance multiple confounding variables, including parity, number of previous births, number of late miscarriages, prior history of cerclage, and cervical length. Differences in pregnancy outcomes between the two groups were compared before and after weighting, including miscarriage, preterm birth, live birth. The impact of cervical cerclage on live birth outcomes was explored using multivariate logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePrior to sIPTW, significant differences existed between the cervical cerclage group and the expectant management group in terms of gestational age, parity, number of late miscarriages, cervical length, and BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After sIPTW, no statistically significant differences were observed between the two groups in clinical data. Both before and after sIPTW, the cervical cerclage group had higher rates of term delivery and preterm delivery than the expectant group, while the miscarriage rate was lower. The live birth rate in the cerclage group was significantly higher than that in the expectant group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate logistic regression analysis demonstrated that cervical cerclage showed a positive correlation with live birth outcomes both before and after sIPTW. Subgroup analysis revealed that the efficacy of cervical cerclage varied among different examination groups (interaction term \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCervical cerclage significantly improves the rates of full-term delivery and live birth among patients with cervical incompetence while reducing the miscarriage rate, particularly in those with abnormal cervical examination.\u003c/p\u003e","manuscriptTitle":"The Impact of Cervical Cerclage on Live Birth Outcomes in Cervical Incompetence: A Real-World Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 17:21:28","doi":"10.21203/rs.3.rs-8704478/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-21T17:18:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-19T03:46:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T08:21:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21814811297321284345671734672919110473","date":"2026-02-23T07:54:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T19:24:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123126628936819172893710035914557076607","date":"2026-02-21T18:39:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T06:59:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-08T02:15:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-02-08T02:09:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c45d46e4-4e9a-4927-ade8-3be4c6f7ad70","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T01:23:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 17:21:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8704478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8704478","identity":"rs-8704478","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}