Evaluating the Impact of a Trial of Labor After Cesarean Section on Labor Duration: A Retrospective Cohort Study

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Trial of Labor After C-section (TOLAC) may reduce the recurrence of cesarean deliveries. Uterine rupture, a complication of TOLAC, is a serious obstetric complication, with prolonged labor duration suggested as a risk factor. However, there are no reports examining labor duration in women with cesarean scars, considering the termination of labor through cesarean section and selection bias. Our study aimed to investigate the impact of cesarean scars on the labor duration of TOLAC patients, considering these factors. Methods: From January 1, 2012, to December 31, 2021, we investigated a cohort of 2,964 individuals who attempted vaginal delivery at a single medical center, categorizing them into TOLAC and non-TOLAC groups. Propensity scores were generated based on factors that could influence labor duration, and survival analysis was conducted after applying Inverse Probability of Treatment Weighting (IPTW). We evaluated the robustness of the results by propensity score matching and bootstrapping as a sensitivity analysis. Results: Results from Cox proportional hazards regression analysis indicated that the unadjusted hazard ratio was 0.83 (95% Confidence Interval (CI): 0.70-0.98, p=0.027), whereas the IPTW-adjusted hazard ratio was 0.98 (95% CI: 0.74-1.30, p=0.91). Conclusion: Factors such as maternal physique and fetal characteristics influence labor duration, with the impact of cesarean scars being limited. Our findings were consistent with previous studies and provided supportive evidence. Labor duration Trial of labor after cesarean section Vaginal delivery Cesarean section Propensity scores IPTW Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The prevalence of cesarean section (C-section) is on the rise globally, with some nations reporting that up to 50% of births are conducted via C-section [ 2 ]. In Japan, the rate has increased to over 18% [ 3 ], and tertiary care facilities indicate that 37.3% of deliveries are C-section [ 4 ]. Such repeated procedures are associated with extended surgical durations, increased risk of severe adhesions, higher blood loss, and elevated transfusion needs [ 5 ]. Furthermore, post-delivery, women may face complications such as infertility, high-risk subsequent pregnancies, postpartum menorrhagia, and dysmenorrhea. These symptoms threaten their quality of life. The Trial of Labor after C-section (TOLAC) offers a promising approach to reduce the frequency of repeat cesarean deliveries and alleviate their adverse effects [ 6 ]. Successful vaginal birth after cesarean section (VBAC) [ 7 ]not only presents lower infection, fever, and postpartum hemorrhage risks but also proves to be more cost-efficient than elective repeat cesarean delivery (ERCD)[ 8 ]. While successful TOLAC is associated with the least maternal morbidity, the hazards of failed TOLAC surpass those of planned repeat C-section [ 9 ]. Factors such as occiput-posterior fetal position, extended second labor stage, maternal age, and large-for-gestational-age fetuses contribute to TOLAC failure [ 10 ]. The primary concern with unsuccessful TOLAC is uterine rupture, which prompts many hospitals to proceed cautiously with TOLAC attempts [ 11 ]. Additionally, the duration of labor, especially the length of the second stage, is a critical risk factor for uterine rupture during TOLAC [ 12 ][ 13 ]. Research on labor duration in women with previous C-section remains limited. A study [ 6 ] focusing on cervical dilation time during the first labor stage reported median durations of 3.0 hours for TOLAC and 2.8 hours for non-TOLAC subjects. However, factors such as maternal age and parity influence labor duration, rendering simple time comparisons potentially biased. Previous investigations of TOLAC labor duration have often overlooked cesarean terminations and failed to adjust for variables affecting labor time. Our study aimed to reveal the impact of cesarean scars on the duration of vaginal labor with these limitations. Methods Study Design This retrospective cohort study utilized data from a single medical center from January 1, 2012, to December 31, 2021. We included singleton pregnancies between 37 weeks 0 days and 41 weeks 6 days, both spontaneous and induced labor. The exclusion criteria were elective cesarean sections, preterm births before 37 weeks, post-term births after 42 weeks, intrauterine fetal death, and multiple gestations. Comprehensive information on maternal background, medical history, delivery details, and postnatal and neonatal care was obtained from electronic health records. Data were extracted according to predefined common categories provided by the Japan Society of Obstetrics and Gynecology Perinatal Database. Participants who underwent vaginal delivery were categorized into TOLAC and non-TOLAC groups based on their history of C-section. Participants who required emergent C-section during the vaginal delivery trial were censored. Labor duration was defined as the total time from labor onset to delivery, encompassing both the first and second stages of labor. The onset of labor was determined based on the participants’ self-reports. Statistical analysis We assumed an effect size of 0.20, alpha error of 0.05, beta error of 0.20, and dropout rate of 5%. Based on the 2021 birth statistics of the facility, we presumed the proportion of TOLAC (Trial of Labor After Cesarean) to be 0.07, resulting in a calculated sample size of 3,007 participants. Influenced by prior studies [ 8 ],[ 14 ]–[ 18 ], we identified 14 factors potentially affecting labor duration. These factors included maternal age, Body Mass Index (BMI), maternal nationality, history of vaginal birth, pre-pregnancy smoking habits, gestational diabetes, premature rupture of membranes, fetal sex, birth weight, fetal position, labor induction, labor analgesia, Kristeller maneuver, and vacuum extraction. The distributions of these variables are presented in Table 1. For each factor, continuous variables were analyzed using the t-test, while categorical variables were examined using the chi-square test. The Standardized Mean Difference (SMD) [ 19 ] was also calculated. Data with missing information on delivery time or with more than 25% missing values for any item were excluded. Given that the database was regularly updated by medical staff immediately after childbirth, missing information was assumed to be Missing At Random (MAR) [ 20 ]. Multiple imputations were employed to address the missing values. Subsequently, logistic regression was used to calculate propensity scores based on these factors. The area under the Receiver Operating Characteristic curve (ROC-AUC) of the propensity scores was computed. For the treatment group, weights were determined as the inverse of the propensity score, while for the control group, weights were the inverse of one minus the propensity score, calculating the Inverse Probability of Treatment Weighting (IPTW) [ 21 ]. These weights were then applied to the dataset. To address the increased variance in estimates due to propensity scores being close to zero or one, we trimmed the top and bottom 1% of the propensity scores. Survival curves for each labor duration were created, from which labor duration curves were depicted, and hazard ratios were estimated using Cox proportional hazards regression analysis. Statistical analyses were conducted using R software (version 4.2.3, R Foundation for Statistical Computing, Vienna, Austria). Outcome The primary outcome was designated as the hazard ratio for labor duration based on the presence of cesarean scars following the application of IPTW. The secondary outcome was determined as the hazard ratio for labor duration associated with cesarean scars without the application of IPTW. Sensitivity analysis IPTW estimates the average treatment effect (ATE) across the entire trial population, including patients with and without cesarean section scars. However, extreme propensity scores can lead to unstable estimates[ 22 ]. Therefore, as a sensitivity analysis, we conducted an assessment using propensity score matching. By matching participants with similar propensity scores from both the exposed and control groups, the distribution of covariates in the matched subset became closer to that in the exposed study population. Propensity score matching and IPTW have different assumptions and limitations, allowing for the strengthening of result robustness by examining the effects in both populations. Considering the potential dependency of the results on a specific dataset, a sensitivity analysis was conducted using the bootstrap method. The bootstrap algorithm can be used to align the values of the explanatory variables with those of a given target distribution [ 23 ]. We randomly resampled the original dataset to generate bootstrap samples. For each sample, Cox proportional hazard models were applied both with and without IPTW to calculate hazard ratios. The distribution of hazard ratios was estimated from the obtained samples. This process was repeated 1,000 times. Results During the observation period, 3,707 individuals experienced childbirth, of which 2,984 attempted vaginal delivery. Of these, 20 were excluded due to incomplete data on labor duration, resulting in 2,964 subjects being included in the analysis. The non-TOLAC group consisted of 2,777 individuals (93.7%), whereas the TOLAC group included 187 individuals (6.3%). During the observation period, 46 individuals (25.4%) in the TOLAC group and 107 individuals (3.9%) in the non-TOLAC group were censored because of emergent C-section (Fig. 1 ). Overall, compared to the non-TOLAC group, the TOLAC group included individuals of older age, and a higher prevalence of gestational diabetes, patients who underwent vacuum delivery, and those who underwent emergency C-section. Individuals with a history of vaginal delivery, those who underwent labor induction, and those who underwent the Kristeller maneuver were more common in the non-TOLAC group. Because more than 25% of the data were missing, the variable for maternal smoking was excluded. The characteristics of the study population are presented in Table 1. The area under the curve (AUC) of the propensity score was 0.80 (95% Confidence Interval (CI): 0.77–0.84). The probability density of the propensity scores for treated and untreated patients is summarized in Fig. 2 . As expected, the distribution of propensity scores for the treatment group shifted towards 1, while that for the untreated group shifted towards 0. Figure 3 displays the labor duration curves for participants stratified by TOLAC without IPTW, along with the 95% CI. The Log-Rank test yielded a result of p = 0.03. According to the Cox proportional hazards analysis, the hazard ratio for TOLAC was 0.83 (95% CI: 0.70–0.98, p = 0.027). Figure 4 shows the labor duration curves for participants stratified by TOLAC with IPTW. The Log-Rank test resulted in p = 0.70. The Cox proportional hazards analysis revealed a hazard ratio for TOLAC of 0.98 (95% CI: 0.74–1.30, p = 0.91). After performing propensity score matching, the hazard ratio was 1.02 (95% Confidence Interval (CI): 0.81–1.28, p = 0.88). The characteristics after matching are presented in Supplementary Table 1, and the survival curves are illustrated in Supplementary Fig. 1. The bootstrap hazard ratio without IPTW was 0.83 (95% CI: 0.70–0.97), and with IPTW was 1.07 (95% CI: 0.87–1.33). Discussion When not adjusting for covariates through IPTW, the duration of labor was significantly longer for those undergoing TOLAC than for those who did not. However, this difference dissipated after applying IPTW and accounting for other factors that potentially influence labor duration. These trends remained consistent even after conducting sensitivity analyses using propensity scores and the bootstrap method. As emphasized in prior research [ 24 ],[ 25 ], the most significant predictor of successful TOLAC is a history of vaginal birth. The likelihood of a successful VBAC increases if there is a previous history of vaginal delivery, previous successful TOLAC, or if natural labor commences during a current pregnancy with attempted TOLAC [ 26 ]. Studies examining the duration of TOLAC labor stratified based on the history of vaginal delivery [ 27 ] indicated that TOLAC groups without a history of vaginal delivery were comparable to nulliparous control groups. Conversely, the TOLAC groups with a history of vaginal delivery were similar to the multiparous control groups, suggesting that the diagnosis of dysfunctional labor should be based on the number of deliveries. Another study focusing on the first phase of the labor curve and the rate of cervical dilation temporarily [ 28 ] found no significant differences between individuals who underwent TOLAC and those without previous cesarean sections. This research concludes that individuals undergoing TOLAC should be diagnosed with labor dysfunction under the same criteria as those without cesarean scars. A study investigating the labor patterns of primary parous women undergoing TOLAC without a history of vaginal delivery and those experiencing natural labor [ 29 ] reported no differences in the first stage of labor. However, the median duration of the second stage of labor was slightly longer in the TOLAC group. The authors could not dismiss the possibility that a higher incidence of fundamental labor complications, such as narrow pelvis in the TOLAC group, contributed to the observed prolongation of labor duration. Our findings did not contradict these observations and provided unified support. Our study was novel in its examination of labor duration through the adjustment of individual profiles. However, this study had several limitations. First, the lack of data regarding the reasons for the previous C-section in the TOLAC group might have introduced selection bias. Nevertheless, we believe that by calculating propensity scores based on detailed maternal and fetal backgrounds and procedures, we were able to compare individuals with similar backgrounds between the TOLAC and non-TOLAC groups after adjustment. Second, the onset of labor was based on self-reporting by pregnant women, which could not eliminate the influence of recall bias. Third, this single-center study targeted a relatively stable population of pregnant women. Fourth, it was not possible to separately evaluate the first and second stages of labor. Conclusion The impact of cesarean scar on the duration of labor was found to be limited in vaginal delivery. Our findings suggest that the duration of labor in patients attempting TOLAC was influenced by factors such as maternal physique, fetal profile, and obstetric maneuvers. Declarations Ethics approval and consent to participate : The local ethics committee approved the research protocol (approval number 2208-018), and informed consent was obtained from each patient. This study complied with the Declaration of Helsinki for human research and provided written informed consent for the use of data. Consent for publication : Not applicable Availability of data and materials : The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests : The authors declare no competing interests. Funding : This study did not receive any financial support. Authors' contributions : Hikaru Ooba contributed to concept development, data curation, formal analysis, investigation, resources, visualization, and manuscript writing. Jota Maki contributed to formal analysis and methodology, manuscript review and editing, and final approval. Hisashi Masuyama contributed to research supervision and project management. Acknowledgments : We acknowledge Dr. Hironori Ito and Dr. Miho Kanemori for their contribution to data collection. References Vogel JP, Betrán AP, Vindevoghel N, Souza JP, Torloni MR, Zhang J et al. Use of the Robson classification to assess caesarean section trends in 21 countries: a secondary analysis of two WHO multicountry surveys. Lancet Glob Health [Internet]. 2015;3(5):e260-70. http://dx.doi.org/10.1016/S2214-109X(15)70094-X . Litorp H, Kidanto HL, Nystrom L, Darj E, Essén B. Increasing caesarean section rates among low-risk groups: a panel study classifying deliveries according to Robson at a university hospital in Tanzania. BMC Pregnancy Childbirth [Internet]. 2013;13:107. http://dx.doi.org/10.1186/1471-2393-13-107 . Maeda E, Ishihara O, Tomio J, Sato A, Terada Y, Kobayashi Y et al. Cesarean section rates and local resources for perinatal care in Japan: A nationwide ecological study using the national database of health insurance claims. J Obstet Gynaecol Res [Internet]. 2018;44(2):208–16. http://dx.doi.org/10.1111/jog.13518 . Ono T, Matsuda Y, Sasaki K, Satoh S, Tsuji S, Kimura F et al. Comparative analysis of cesarean section rates using Robson Ten-Group Classification System and Lorenz curve in the main institutions in Japan. J Obstet Gynaecol Res [Internet]. 2016;42(10):1279–85. http://dx.doi.org/10.1111/jog.13069 . Lyell DJ. Adhesions and perioperative complications of repeat cesarean delivery. Am J Obstet Gynecol [Internet]. 2011;205(6 Suppl):S11-8. http://dx.doi.org/10.1016/j.ajog.2011.09.029 . Graseck AS, Odibo AO, Tuuli M, Roehl KA, Macones GA, Cahill AG. Normal first stage of labor in women undergoing trial of labor after cesarean delivery. Obstet Gynecol [Internet]. 2012;119(4):732–6. http://dx.doi.org/10.1097/AOG.0b013e31824c096c . American College of Obstetricians and Gynecologists’ Committee on Practice Bulletins-Obstetrics. ACOG practice bulletin no. 205: Vaginal birth after cesarean delivery. Obstet Gynecol [Internet]. 2019;133(2):e110–27. Available from: https://pubmed.ncbi.nlm.nih.gov/30681543/ . Lin J, Hou Y, Ke Y, Zeng W, Gu W. Establishment and validation of a prediction model for vaginal delivery after cesarean and its pregnancy outcomes-Based on a prospective study. Eur J Obstet Gynecol Reprod Biol [Internet]. 2019;242:114–21. http://dx.doi.org/10.1016/j.ejogrb.2019.09.015 . Place K, Kruit H, Tekay A, Heinonen S, Rahkonen L. Success of trial of labor in women with a history of previous cesarean section for failed labor induction or labor dystocia: a retrospective cohort study. BMC Pregnancy Childbirth [Internet]. 2019;19(1):176. http://dx.doi.org/10.1186/s12884-019-2334-3 . Melamed N, Segev M, Hadar E, Peled Y, Wiznitzer A, Yogev Y. Outcome of trial of labor after cesarean section in women with past failed operative vaginal delivery. Am J Obstet Gynecol [Internet]. 2013;209(1):49.e1-7. http://dx.doi.org/10.1016/j.ajog.2013.03.010 . Wan S, Yang M, Pei J, Zhao X, Zhou C, Wu Y et al. Pregnancy outcomes and associated factors for uterine rupture: an 8 years population-based retrospective study. BMC Pregnancy Childbirth [Internet]. 2022;22(1):91. http://dx.doi.org/10.1186/s12884-022-04415-6 . Deshmukh U, Denoble AE, Son M. Trial of labor after cesarean, vaginal birth after cesarean, and the risk of uterine rupture: an expert review. Am J Obstet Gynecol [Internet]. 2023; https://doi.org/10.1016/j.ajog.2022.10.030 . Hehir MP, Rouse DJ, Miller RS, Ananth CV, Wright JD, Siddiq Z et al. Second-Stage Duration and Outcomes Among Women Who Labored After a Prior Cesarean Delivery. Obstet Gynecol [Internet]. 2018;131(3):514–22. http://dx.doi.org/10.1097/AOG.0000000000002478 . Srinivas SK, Stamilio DM, Sammel MD, Stevens EJ, Peipert JF, Odibo AO et al. Vaginal birth after caesarean delivery: does maternal age affect safety and success? Paediatr Perinat Epidemiol [Internet]. 2007;21(2):114–20. http://dx.doi.org/10.1111/j.1365-3016.2007.00794.x . Bujold E, Blackwell SC, Gauthier RJ. Cervical ripening with transcervical foley catheter and the risk of uterine rupture. Obstet Gynecol [Internet]. 2004;103(1):18–23. http://dx.doi.org/10.1097/01.AOG.0000109148.23082.C1 . Carroll CS, Sr, Magann EF, Chauhan SP, Klauser CK, Morrison JC. Vaginal birth after cesarean section versus elective repeat cesarean delivery: Weight-based outcomes. Am J Obstet Gynecol [Internet]. 2003;188(6):1516–20; discussion 1520-2. http://dx.doi.org/10.1067/mob.2003.472 . Hibbard JU, Gilbert S, Landon MB, Hauth JC, Leveno KJ, Spong CY et al. Trial of labor or repeat cesarean delivery in women with morbid obesity and previous cesarean delivery. Obstet Gynecol [Internet]. 2006;108(1):125–33. http://dx.doi.org/10.1097/01.AOG.0000223871.69852.31 . Levin G, Tsur A, Tenenbaum L, Mor N, Zamir M, Meyer R. Prediction of successful trial of labor after cesarean among grand-multiparous women. Arch Gynecol Obstet [Internet]. 2021; http://dx.doi.org/10.1007/s00404-021-06311-4 . Zhang Z, Kim HJ, Lonjon G, Zhu Y, written on behalf of AME Big-Data Clinical Trial Collaborative Group. Balance diagnostics after propensity score matching. Ann Transl Med [Internet]. 2019;7(1):16. http://dx.doi.org/10.21037/atm.2018.12.10 . Pedersen AB, Mikkelsen EM, Cronin-Fenton D, Kristensen NR, Pham TM, Pedersen L et al. Missing data and multiple imputation in clinical epidemiological research. Clin Epidemiol [Internet]. 2017;9:157–66. http://dx.doi.org/10.2147/CLEP.S129785 . Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med [Internet]. 2015;34(28):3661–79. http://dx.doi.org/10.1002/sim.6607 . Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol [Internet]. 2006;163(3):262–70. http://dx.doi.org/10.1093/aje/kwj047 . Haukoos JS, Lewis RJ. Advanced statistics: bootstrapping confidence intervals for statistics with difficult distributions. Acad Emerg Med [Internet]. 2005;12(4):360–5. http://dx.doi.org/10.1197/j.aem.2004.11.018 . Landon MB, Grobman WA, Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal–Fetal Medicine Units Network. What We Have Learned About Trial of Labor After Cesarean Delivery from the Maternal-Fetal Medicine Units Cesarean Registry. Semin Perinatol [Internet]. 2016;40(5):281–6. http://dx.doi.org/10.1053/j.semperi.2016.03.003 . Abildgaard H, Ingerslev MD, Nickelsen C, Secher NJ. Cervical dilation at the time of cesarean section for dystocia -- effect on subsequent trial of labor. Acta Obstet Gynecol Scand [Internet]. 2013;92(2):193–7. http://dx.doi.org/10.1111/aogs.12023 . Cahill AG, Stamilio DM, Odibo AO, Peipert JF, Ratcliffe SJ, Stevens EJ et al. Is vaginal birth after cesarean (VBAC) or elective repeat cesarean safer in women with a prior vaginal delivery? Am J Obstet Gynecol [Internet]. 2006;195(4):1143–7. http://dx.doi.org/10.1016/j.ajog.2006.06.045 . Landon MB, Leindecker S, Spong CY, Hauth JC, Bloom S, Varner MW et al. The MFMU Cesarean Registry: factors affecting the success of trial of labor after previous cesarean delivery. Am J Obstet Gynecol [Internet]. 2005;193(3 Pt 2):1016–23. http://dx.doi.org/10.1016/j.ajog.2005.05.066 . Chazotte C, Madden R, Cohen WR. Labor patterns in women with previous cesareans. Obstet Gynecol [Internet]. 1990;75(3 Pt 1):350–5. Available from: https://www.ncbi.nlm.nih.gov/pubmed/2304706 . Grantz KL, Gonzalez-Quintero V, Troendle J, Reddy UM, Hinkle SN, Kominiarek MA et al. Labor patterns in women attempting vaginal birth after cesarean with normal neonatal outcomes. Am J Obstet Gynecol [Internet]. 2015;213(2):226.e1-6. http://dx.doi.org/10.1016/j.ajog.2015.04.033 . Tables Table1. Participant Background 1 n (%) Characteristic N NonTOLAC N = 2,777 1 TOLAC N = 187 1 SMD 2 95% CI 23 p-value 4 Maternal age 2,964 -0.41 -0.56, -0.26 <0.001 Mean (SD) 31 (5) 33 (5) Maternal body mass index 2,914 -0.13 -0.28, 0.02 0.077 Mean (SD) 25.4 (3.6) 25.9 (3.4) Unknown 46 4 Maternal nationality 2,964 0.10 -0.04, 0.25 0.2 Japanese 2,626 (95%) 172 (92%) Other Nationality 151 (5.4%) 15 (8.0%) History of vaginal delivery 2,964 0.53 0.38, 0.68 <0.001 ( - ) 1,490 (54%) 146 (78%) ( + ) 1,287 (46%) 41 (22%) Smoking 2,037 0.04 -0.14, 0.22 0.8 ( - ) 1,611 (84%) 108 (83%) ( + ) 296 (16%) 22 (17%) Unknown 870 57 Gestational diabetes mellitus 2,964 0.23 0.08, 0.38 <0.001 ( - ) 2,586 (93%) 161 (86%) ( + ) 191 (6.9%) 26 (14%) Premature rupture of membranes 2,964 0.04 -0.11, 0.19 0.7 ( - ) 1,895 (68%) 131 (70%) ( + ) 882 (32%) 56 (30%) Fetal sex 2,964 0.04 -0.11, 0.19 0.6 Female 1,364 (49%) 88 (47%) Male 1,413 (51%) 99 (53%) Fetal birth weight 2,964 0.01 -0.13, 0.16 0.9 Mean (SD) 3,097 (382) 3,091 (440) Fetal position 2,961 0.10 -0.05, 0.25 0.4 Cephalic position 2,730 (98%) 186 (99%) breech presentation 44 (1.6%) 1 (0.5%) Unknown 3 0 Labor induction 2,956 0.50 0.36, 0.65 <0.001 ( - ) 2,260 (82%) 181 (97%) ( + ) 509 (18%) 6 (3.2%) Unknown 8 0 Labor analgesia 2,964 0.04 -0.11, 0.19 0.7 ( - ) 2,582 (93%) 172 (92%) ( + ) 195 (7.0%) 15 (8.0%) Vacuum extraction 2,811 0.24 0.07, 0.41 0.003 ( - ) 2,441 (91%) 118 (84%) ( + ) 229 (8.6%) 23 (16%) Unknown 107 46 Kristeller maneuver 2,963 0.40 0.25, 0.55 <0.001 ( - ) 2,570 (93%) 187 (100%) ( + ) 206 (7.4%) 0 (0%) Unknown 1 0 Censor 2,964 0.62 0.47, 0.77 <0.001 ( - ) 2,670 (96%) 141 (75%) ( + ) 107 (3.9%) 46 (25%) Duration of delivery 2,964 0.07 -0.08, 0.21 0.4 Mean (SD) 614 (563) 576 (597) 2 Standardized Mean Difference 3 CI = Confidence Interval 4 Welch Two Sample t-test; Pearson's Chi-squared test Additional Declarations No competing interests reported. Supplementary Files Supplementaltable1.docx Supplementaryfigure1.tif Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 15 Aug, 2024 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 25 Mar, 2024 Reviews received at journal 24 Mar, 2024 Reviewers agreed at journal 13 Mar, 2024 Reviewers invited by journal 12 Mar, 2024 Editor assigned by journal 12 Mar, 2024 Editor invited by journal 06 Mar, 2024 Submission checks completed at journal 06 Mar, 2024 First submitted to journal 28 Feb, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-3996134","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276731134,"identity":"c415e4df-2b70-49cd-a778-7eed65e97c2b","order_by":0,"name":"Hikaru Ooba","email":"","orcid":"","institution":"Okayama University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hikaru","middleName":"","lastName":"Ooba","suffix":""},{"id":276731135,"identity":"392808cb-00fe-4dc2-8a68-1d8693e36737","order_by":1,"name":"Jota Maki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBACxgYwlcDDz8CGIsGGRTFMCzNEi2QDsVoYGCBaGAwO4FOEoqG9/+CnGzVpMsbH2xI/89Rsk2eQSGD88IOBLw+nw3oOM0vnHMvhMTtz7LA0z7Hbhg0SCcySPQxsxTi1zEhmkM5hq+Axu5HeIM3Ddptx/40EBmmgXxIbcGth/p3zr4LHeEZ682+ef7ftQbb8JqCFTTq3LYfHQCLtmDRv2+1EoBY2/Lb0HDazzu1L45E4cyzNcm7f7eQGnodtlj0GuP1i2N74+HbOt2R7/vY24xtvvt22bWBPPnzjR8UxnCFmiGw9Ew/EZqCYwbEEXFrkUVz5A8GuwallFIyCUTAKRhwAAMvVU+/GeM5sAAAAAElFTkSuQmCC","orcid":"","institution":"Okayama University Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jota","middleName":"","lastName":"Maki","suffix":""},{"id":276731136,"identity":"4c54fa5f-9991-4bce-a2b4-768d04e8df65","order_by":2,"name":"Hisashi Masuyama","email":"","orcid":"","institution":"Okayama University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hisashi","middleName":"","lastName":"Masuyama","suffix":""}],"badges":[],"createdAt":"2024-02-28 08:44:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3996134/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3996134/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-024-06744-0","type":"published","date":"2024-08-15T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52302547,"identity":"9e0ec876-91b3-4c8b-bdff-b29485e27278","added_by":"auto","created_at":"2024-03-08 18:46:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132285,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant selection flow. TOLAC: Trial of labor after cesarean section; VBAC: Vaginal delivery after cesarean section.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/ae1aba0daf707737b73ee09f.png"},{"id":52302549,"identity":"165c8424-3465-46ca-8699-a08cb1930a22","added_by":"auto","created_at":"2024-03-08 18:46:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112778,"visible":true,"origin":"","legend":"\u003cp\u003eProbability density of propensity scores.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/b3f7c2f3ba3043b485cea89a.png"},{"id":52303109,"identity":"4d37ee2b-7ff4-40d3-8fe1-89440fe125c0","added_by":"auto","created_at":"2024-03-08 18:54:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237170,"visible":true,"origin":"","legend":"\u003cp\u003eLabor time curve for participants stratified by TOLAC without IPTW.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/59cd53de6563b7e19c25fb52.png"},{"id":52302552,"identity":"0360ee0a-54e0-4a61-b555-fa1cc2849f8b","added_by":"auto","created_at":"2024-03-08 18:46:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":240037,"visible":true,"origin":"","legend":"\u003cp\u003eLabor time curve for participants stratified by TOLAC with IPTW.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/95883110c9f430813380112f.png"},{"id":63071323,"identity":"1a6a972a-e112-4b83-90d0-ffd4632540c6","added_by":"auto","created_at":"2024-08-22 20:06:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":888585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/41254222-cf35-4fe7-a3c5-c555d6940f18.pdf"},{"id":52302548,"identity":"8543e1e4-75fa-4d86-913e-59b22d5256af","added_by":"auto","created_at":"2024-03-08 18:46:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25643,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaltable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/731312cae97ac85c957a11ba.docx"},{"id":52302554,"identity":"7281dd2f-11c6-4e69-a4ae-a595c46e94a9","added_by":"auto","created_at":"2024-03-08 18:47:00","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10941092,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/01fc915d6019f0967ff391c0.tif"},{"id":52302551,"identity":"7c563a12-c99b-4847-ba53-0c8c6ede271f","added_by":"auto","created_at":"2024-03-08 18:46:59","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":181071,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3996134/v1/468e4d8111d8f25ba0148b9b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the Impact of a Trial of Labor After Cesarean Section on Labor Duration: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe prevalence of cesarean section (C-section) is on the rise globally, with some nations reporting that up to 50% of births are conducted via C-section [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Japan, the rate has increased to over 18% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and tertiary care facilities indicate that 37.3% of deliveries are C-section [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Such repeated procedures are associated with extended surgical durations, increased risk of severe adhesions, higher blood loss, and elevated transfusion needs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, post-delivery, women may face complications such as infertility, high-risk subsequent pregnancies, postpartum menorrhagia, and dysmenorrhea. These symptoms threaten their quality of life.\u003c/p\u003e\u003cp\u003eThe Trial of Labor after C-section (TOLAC) offers a promising approach to reduce the frequency of repeat cesarean deliveries and alleviate their adverse effects [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Successful vaginal birth after cesarean section (VBAC) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]not only presents lower infection, fever, and postpartum hemorrhage risks but also proves to be more cost-efficient than elective repeat cesarean delivery (ERCD)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While successful TOLAC is associated with the least maternal morbidity, the hazards of failed TOLAC surpass those of planned repeat C-section [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Factors such as occiput-posterior fetal position, extended second labor stage, maternal age, and large-for-gestational-age fetuses contribute to TOLAC failure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The primary concern with unsuccessful TOLAC is uterine rupture, which prompts many hospitals to proceed cautiously with TOLAC attempts [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, the duration of labor, especially the length of the second stage, is a critical risk factor for uterine rupture during TOLAC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eResearch on labor duration in women with previous C-section remains limited. A study [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] focusing on cervical dilation time during the first labor stage reported median durations of 3.0 hours for TOLAC and 2.8 hours for non-TOLAC subjects. However, factors such as maternal age and parity influence labor duration, rendering simple time comparisons potentially biased. Previous investigations of TOLAC labor duration have often overlooked cesarean terminations and failed to adjust for variables affecting labor time. Our study aimed to reveal the impact of cesarean scars on the duration of vaginal labor with these limitations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study utilized data from a single medical center from January 1, 2012, to December 31, 2021. We included singleton pregnancies between 37 weeks 0 days and 41 weeks 6 days, both spontaneous and induced labor. The exclusion criteria were elective cesarean sections, preterm births before 37 weeks, post-term births after 42 weeks, intrauterine fetal death, and multiple gestations. Comprehensive information on maternal background, medical history, delivery details, and postnatal and neonatal care was obtained from electronic health records. Data were extracted according to predefined common categories provided by the Japan Society of Obstetrics and Gynecology Perinatal Database. Participants who underwent vaginal delivery were categorized into TOLAC and non-TOLAC groups based on their history of C-section. Participants who required emergent C-section during the vaginal delivery trial were censored. Labor duration was defined as the total time from labor onset to delivery, encompassing both the first and second stages of labor. The onset of labor was determined based on the participants\u0026rsquo; self-reports.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe assumed an effect size of 0.20, alpha error of 0.05, beta error of 0.20, and dropout rate of 5%. Based on the 2021 birth statistics of the facility, we presumed the proportion of TOLAC (Trial of Labor After Cesarean) to be 0.07, resulting in a calculated sample size of 3,007 participants. Influenced by prior studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e],[\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], we identified 14 factors potentially affecting labor duration. These factors included maternal age, Body Mass Index (BMI), maternal nationality, history of vaginal birth, pre-pregnancy smoking habits, gestational diabetes, premature rupture of membranes, fetal sex, birth weight, fetal position, labor induction, labor analgesia, Kristeller maneuver, and vacuum extraction. The distributions of these variables are presented in Table\u0026nbsp;1. For each factor, continuous variables were analyzed using the t-test, while categorical variables were examined using the chi-square test. The Standardized Mean Difference (SMD) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was also calculated. Data with missing information on delivery time or with more than 25% missing values for any item were excluded. Given that the database was regularly updated by medical staff immediately after childbirth, missing information was assumed to be Missing At Random (MAR) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Multiple imputations were employed to address the missing values. Subsequently, logistic regression was used to calculate propensity scores based on these factors. The area under the Receiver Operating Characteristic curve (ROC-AUC) of the propensity scores was computed. For the treatment group, weights were determined as the inverse of the propensity score, while for the control group, weights were the inverse of one minus the propensity score, calculating the Inverse Probability of Treatment Weighting (IPTW) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These weights were then applied to the dataset. To address the increased variance in estimates due to propensity scores being close to zero or one, we trimmed the top and bottom 1% of the propensity scores. Survival curves for each labor duration were created, from which labor duration curves were depicted, and hazard ratios were estimated using Cox proportional hazards regression analysis. Statistical analyses were conducted using R software (version 4.2.3, R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eOutcome\u003c/h2\u003e\u003cp\u003eThe primary outcome was designated as the hazard ratio for labor duration based on the presence of cesarean scars following the application of IPTW. The secondary outcome was determined as the hazard ratio for labor duration associated with cesarean scars without the application of IPTW.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\u003cp\u003eIPTW estimates the average treatment effect (ATE) across the entire trial population, including patients with and without cesarean section scars. However, extreme propensity scores can lead to unstable estimates[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, as a sensitivity analysis, we conducted an assessment using propensity score matching. By matching participants with similar propensity scores from both the exposed and control groups, the distribution of covariates in the matched subset became closer to that in the exposed study population. Propensity score matching and IPTW have different assumptions and limitations, allowing for the strengthening of result robustness by examining the effects in both populations.\u003c/p\u003e\u003cp\u003eConsidering the potential dependency of the results on a specific dataset, a sensitivity analysis was conducted using the bootstrap method. The bootstrap algorithm can be used to align the values of the explanatory variables with those of a given target distribution [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We randomly resampled the original dataset to generate bootstrap samples. For each sample, Cox proportional hazard models were applied both with and without IPTW to calculate hazard ratios. The distribution of hazard ratios was estimated from the obtained samples. This process was repeated 1,000 times.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the observation period, 3,707 individuals experienced childbirth, of which 2,984 attempted vaginal delivery. Of these, 20 were excluded due to incomplete data on labor duration, resulting in 2,964 subjects being included in the analysis. The non-TOLAC group consisted of 2,777 individuals (93.7%), whereas the TOLAC group included 187 individuals (6.3%). During the observation period, 46 individuals (25.4%) in the TOLAC group and 107 individuals (3.9%) in the non-TOLAC group were censored because of emergent C-section (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, compared to the non-TOLAC group, the TOLAC group included individuals of older age, and a higher prevalence of gestational diabetes, patients who underwent vacuum delivery, and those who underwent emergency C-section. Individuals with a history of vaginal delivery, those who underwent labor induction, and those who underwent the Kristeller maneuver were more common in the non-TOLAC group. Because more than 25% of the data were missing, the variable for maternal smoking was excluded. The characteristics of the study population are presented in Table\u0026nbsp;1. The area under the curve (AUC) of the propensity score was 0.80 (95% Confidence Interval (CI): 0.77\u0026ndash;0.84). The probability density of the propensity scores for treated and untreated patients is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As expected, the distribution of propensity scores for the treatment group shifted towards 1, while that for the untreated group shifted towards 0. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the labor duration curves for participants stratified by TOLAC without IPTW, along with the 95% CI. The Log-Rank test yielded a result of p\u0026thinsp;=\u0026thinsp;0.03. According to the Cox proportional hazards analysis, the hazard ratio for TOLAC was 0.83 (95% CI: 0.70\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.027). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the labor duration curves for participants stratified by TOLAC with IPTW. The Log-Rank test resulted in p\u0026thinsp;=\u0026thinsp;0.70. The Cox proportional hazards analysis revealed a hazard ratio for TOLAC of 0.98 (95% CI: 0.74\u0026ndash;1.30, p\u0026thinsp;=\u0026thinsp;0.91). After performing propensity score matching, the hazard ratio was 1.02 (95% Confidence Interval (CI): 0.81\u0026ndash;1.28, p\u0026thinsp;=\u0026thinsp;0.88). The characteristics after matching are presented in Supplementary Table\u0026nbsp;1, and the survival curves are illustrated in Supplementary Fig.\u0026nbsp;1. The bootstrap hazard ratio without IPTW was 0.83 (95% CI: 0.70\u0026ndash;0.97), and with IPTW was 1.07 (95% CI: 0.87\u0026ndash;1.33).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhen not adjusting for covariates through IPTW, the duration of labor was significantly longer for those undergoing TOLAC than for those who did not. However, this difference dissipated after applying IPTW and accounting for other factors that potentially influence labor duration. These trends remained consistent even after conducting sensitivity analyses using propensity scores and the bootstrap method.\u003c/p\u003e\u003cp\u003eAs emphasized in prior research [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e],[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the most significant predictor of successful TOLAC is a history of vaginal birth. The likelihood of a successful VBAC increases if there is a previous history of vaginal delivery, previous successful TOLAC, or if natural labor commences during a current pregnancy with attempted TOLAC [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Studies examining the duration of TOLAC labor stratified based on the history of vaginal delivery [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] indicated that TOLAC groups without a history of vaginal delivery were comparable to nulliparous control groups. Conversely, the TOLAC groups with a history of vaginal delivery were similar to the multiparous control groups, suggesting that the diagnosis of dysfunctional labor should be based on the number of deliveries. Another study focusing on the first phase of the labor curve and the rate of cervical dilation temporarily [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] found no significant differences between individuals who underwent TOLAC and those without previous cesarean sections. This research concludes that individuals undergoing TOLAC should be diagnosed with labor dysfunction under the same criteria as those without cesarean scars. A study investigating the labor patterns of primary parous women undergoing TOLAC without a history of vaginal delivery and those experiencing natural labor [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] reported no differences in the first stage of labor. However, the median duration of the second stage of labor was slightly longer in the TOLAC group. The authors could not dismiss the possibility that a higher incidence of fundamental labor complications, such as narrow pelvis in the TOLAC group, contributed to the observed prolongation of labor duration. Our findings did not contradict these observations and provided unified support.\u003c/p\u003e\u003cp\u003eOur study was novel in its examination of labor duration through the adjustment of individual profiles. However, this study had several limitations. First, the lack of data regarding the reasons for the previous C-section in the TOLAC group might have introduced selection bias. Nevertheless, we believe that by calculating propensity scores based on detailed maternal and fetal backgrounds and procedures, we were able to compare individuals with similar backgrounds between the TOLAC and non-TOLAC groups after adjustment. Second, the onset of labor was based on self-reporting by pregnant women, which could not eliminate the influence of recall bias. Third, this single-center study targeted a relatively stable population of pregnant women. Fourth, it was not possible to separately evaluate the first and second stages of labor.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe impact of cesarean scar on the duration of labor was found to be limited in vaginal delivery. Our findings suggest that the duration of labor in patients attempting TOLAC was influenced by factors such as maternal physique, fetal profile, and obstetric maneuvers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: The local ethics committee approved the research protocol (approval number 2208-018), and informed consent was obtained from each patient. This study complied with the Declaration of Helsinki for human research and provided written informed consent for the use of data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e:\u0026nbsp;The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study did not receive any financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: Hikaru Ooba contributed to concept development, data curation, formal analysis, investigation, resources, visualization, and manuscript writing. Jota Maki contributed to formal analysis and methodology, manuscript review and editing, and final approval. Hisashi Masuyama contributed to research supervision and project management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: We acknowledge Dr. Hironori Ito and Dr. Miho Kanemori for their contribution to data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVogel JP, Betr\u0026aacute;n AP, Vindevoghel N, Souza JP, Torloni MR, Zhang J et al. Use of the Robson classification to assess caesarean section trends in 21 countries: a secondary analysis of two WHO multicountry surveys. Lancet Glob Health [Internet]. 2015;3(5):e260-70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/S2214-109X(15)70094-X\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(15)70094-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLitorp H, Kidanto HL, Nystrom L, Darj E, Ess\u0026eacute;n B. Increasing caesarean section rates among low-risk groups: a panel study classifying deliveries according to Robson at a university hospital in Tanzania. BMC Pregnancy Childbirth [Internet]. 2013;13:107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/1471-2393-13-107\u003c/span\u003e\u003cspan address=\"10.1186/1471-2393-13-107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaeda E, Ishihara O, Tomio J, Sato A, Terada Y, Kobayashi Y et al. Cesarean section rates and local resources for perinatal care in Japan: A nationwide ecological study using the national database of health insurance claims. J Obstet Gynaecol Res [Internet]. 2018;44(2):208\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1111/jog.13518\u003c/span\u003e\u003cspan address=\"10.1111/jog.13518\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOno T, Matsuda Y, Sasaki K, Satoh S, Tsuji S, Kimura F et al. Comparative analysis of cesarean section rates using Robson Ten-Group Classification System and Lorenz curve in the main institutions in Japan. J Obstet Gynaecol Res [Internet]. 2016;42(10):1279\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1111/jog.13069\u003c/span\u003e\u003cspan address=\"10.1111/jog.13069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLyell DJ. Adhesions and perioperative complications of repeat cesarean delivery. Am J Obstet Gynecol [Internet]. 2011;205(6 Suppl):S11-8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ajog.2011.09.029\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2011.09.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGraseck AS, Odibo AO, Tuuli M, Roehl KA, Macones GA, Cahill AG. Normal first stage of labor in women undergoing trial of labor after cesarean delivery. Obstet Gynecol [Internet]. 2012;119(4):732\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1097/AOG.0b013e31824c096c\u003c/span\u003e\u003cspan address=\"10.1097/AOG.0b013e31824c096c\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmerican College of Obstetricians and Gynecologists\u0026rsquo; Committee on Practice Bulletins-Obstetrics. ACOG practice bulletin no. 205: Vaginal birth after cesarean delivery. Obstet Gynecol [Internet]. 2019;133(2):e110\u0026ndash;27. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30681543/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30681543/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin J, Hou Y, Ke Y, Zeng W, Gu W. Establishment and validation of a prediction model for vaginal delivery after cesarean and its pregnancy outcomes-Based on a prospective study. Eur J Obstet Gynecol Reprod Biol [Internet]. 2019;242:114\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ejogrb.2019.09.015\u003c/span\u003e\u003cspan address=\"10.1016/j.ejogrb.2019.09.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlace K, Kruit H, Tekay A, Heinonen S, Rahkonen L. Success of trial of labor in women with a history of previous cesarean section for failed labor induction or labor dystocia: a retrospective cohort study. BMC Pregnancy Childbirth [Internet]. 2019;19(1):176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12884-019-2334-3\u003c/span\u003e\u003cspan address=\"10.1186/s12884-019-2334-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelamed N, Segev M, Hadar E, Peled Y, Wiznitzer A, Yogev Y. Outcome of trial of labor after cesarean section in women with past failed operative vaginal delivery. Am J Obstet Gynecol [Internet]. 2013;209(1):49.e1-7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ajog.2013.03.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2013.03.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWan S, Yang M, Pei J, Zhao X, Zhou C, Wu Y et al. Pregnancy outcomes and associated factors for uterine rupture: an 8 years population-based retrospective study. BMC Pregnancy Childbirth [Internet]. 2022;22(1):91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12884-022-04415-6\u003c/span\u003e\u003cspan address=\"10.1186/s12884-022-04415-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeshmukh U, Denoble AE, Son M. Trial of labor after cesarean, vaginal birth after cesarean, and the risk of uterine rupture: an expert review. Am J Obstet Gynecol [Internet]. 2023; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajog.2022.10.030\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2022.10.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHehir MP, Rouse DJ, Miller RS, Ananth CV, Wright JD, Siddiq Z et al. Second-Stage Duration and Outcomes Among Women Who Labored After a Prior Cesarean Delivery. Obstet Gynecol [Internet]. 2018;131(3):514\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1097/AOG.0000000000002478\u003c/span\u003e\u003cspan address=\"10.1097/AOG.0000000000002478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrinivas SK, Stamilio DM, Sammel MD, Stevens EJ, Peipert JF, Odibo AO et al. Vaginal birth after caesarean delivery: does maternal age affect safety and success? Paediatr Perinat Epidemiol [Internet]. 2007;21(2):114\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1111/j.1365-3016.2007.00794.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3016.2007.00794.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBujold E, Blackwell SC, Gauthier RJ. Cervical ripening with transcervical foley catheter and the risk of uterine rupture. Obstet Gynecol [Internet]. 2004;103(1):18\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1097/01.AOG.0000109148.23082.C1\u003c/span\u003e\u003cspan address=\"10.1097/01.AOG.0000109148.23082.C1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarroll CS, Sr, Magann EF, Chauhan SP, Klauser CK, Morrison JC. Vaginal birth after cesarean section versus elective repeat cesarean delivery: Weight-based outcomes. Am J Obstet Gynecol [Internet]. 2003;188(6):1516\u0026ndash;20; discussion 1520-2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1067/mob.2003.472\u003c/span\u003e\u003cspan address=\"10.1067/mob.2003.472\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHibbard JU, Gilbert S, Landon MB, Hauth JC, Leveno KJ, Spong CY et al. Trial of labor or repeat cesarean delivery in women with morbid obesity and previous cesarean delivery. Obstet Gynecol [Internet]. 2006;108(1):125\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1097/01.AOG.0000223871.69852.31\u003c/span\u003e\u003cspan address=\"10.1097/01.AOG.0000223871.69852.31\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevin G, Tsur A, Tenenbaum L, Mor N, Zamir M, Meyer R. Prediction of successful trial of labor after cesarean among grand-multiparous women. Arch Gynecol Obstet [Internet]. 2021; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1007/s00404-021-06311-4\u003c/span\u003e\u003cspan address=\"10.1007/s00404-021-06311-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z, Kim HJ, Lonjon G, Zhu Y, written on behalf of AME Big-Data Clinical Trial Collaborative Group. Balance diagnostics after propensity score matching. Ann Transl Med [Internet]. 2019;7(1):16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.21037/atm.2018.12.10\u003c/span\u003e\u003cspan address=\"10.21037/atm.2018.12.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePedersen AB, Mikkelsen EM, Cronin-Fenton D, Kristensen NR, Pham TM, Pedersen L et al. Missing data and multiple imputation in clinical epidemiological research. Clin Epidemiol [Internet]. 2017;9:157\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.2147/CLEP.S129785\u003c/span\u003e\u003cspan address=\"10.2147/CLEP.S129785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAustin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med [Internet]. 2015;34(28):3661\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1002/sim.6607\u003c/span\u003e\u003cspan address=\"10.1002/sim.6607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol [Internet]. 2006;163(3):262\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1093/aje/kwj047\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwj047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaukoos JS, Lewis RJ. Advanced statistics: bootstrapping confidence intervals for statistics with difficult distributions. Acad Emerg Med [Internet]. 2005;12(4):360\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1197/j.aem.2004.11.018\u003c/span\u003e\u003cspan address=\"10.1197/j.aem.2004.11.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandon MB, Grobman WA, Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal\u0026ndash;Fetal Medicine Units Network. What We Have Learned About Trial of Labor After Cesarean Delivery from the Maternal-Fetal Medicine Units Cesarean Registry. Semin Perinatol [Internet]. 2016;40(5):281\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1053/j.semperi.2016.03.003\u003c/span\u003e\u003cspan address=\"10.1053/j.semperi.2016.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbildgaard H, Ingerslev MD, Nickelsen C, Secher NJ. Cervical dilation at the time of cesarean section for dystocia -- effect on subsequent trial of labor. Acta Obstet Gynecol Scand [Internet]. 2013;92(2):193\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1111/aogs.12023\u003c/span\u003e\u003cspan address=\"10.1111/aogs.12023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCahill AG, Stamilio DM, Odibo AO, Peipert JF, Ratcliffe SJ, Stevens EJ et al. Is vaginal birth after cesarean (VBAC) or elective repeat cesarean safer in women with a prior vaginal delivery? Am J Obstet Gynecol [Internet]. 2006;195(4):1143\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ajog.2006.06.045\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2006.06.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandon MB, Leindecker S, Spong CY, Hauth JC, Bloom S, Varner MW et al. The MFMU Cesarean Registry: factors affecting the success of trial of labor after previous cesarean delivery. Am J Obstet Gynecol [Internet]. 2005;193(3 Pt 2):1016\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ajog.2005.05.066\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2005.05.066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChazotte C, Madden R, Cohen WR. Labor patterns in women with previous cesareans. Obstet Gynecol [Internet]. 1990;75(3 Pt 1):350\u0026ndash;5. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pubmed/2304706\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pubmed/2304706\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrantz KL, Gonzalez-Quintero V, Troendle J, Reddy UM, Hinkle SN, Kominiarek MA et al. Labor patterns in women attempting vaginal birth after cesarean with normal neonatal outcomes. Am J Obstet Gynecol [Internet]. 2015;213(2):226.e1-6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.ajog.2015.04.033\u003c/span\u003e\u003cspan address=\"10.1016/j.ajog.2015.04.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable1. Participant Background \u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonTOLAC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 2,777\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOLAC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 187\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSMD\u003c/strong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.56, -0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e31 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e33 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal body mass index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.28, 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e25.4 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e25.9 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal nationality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.04, 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eJapanese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,626 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e172 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eOther Nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e151 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e15 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of vaginal delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e0.38, 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e1,490 (54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e146 (78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e1,287 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e41 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.14, 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e1,611 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e108 (83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e296 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e22 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational diabetes mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e0.08, 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,586 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e161 (86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e191 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e26 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremature rupture of membranes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11, 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e1,895 (68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e131 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e882 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e56 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFetal sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11, 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e1,364 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e88 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e1,413 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e99 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFetal birth weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.13, 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e3,097 (382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e3,091 (440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFetal position\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.05, 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eCephalic position\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,730 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e186 (99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003ebreech presentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e44 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabor induction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e0.36, 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,260 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e181 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e509 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e6 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabor analgesia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.11, 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,582 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e172 (92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e195 (7.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e15 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVacuum extraction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e0.07, 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,441 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e118 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e229 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e23 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eKristeller maneuver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e0.25, 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,570 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e187 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e206 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCensor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e0.47, 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( - )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e2,670 (96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e141 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e( + )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e107 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e46 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e2,964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e-0.08, 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.96132596685083%\" valign=\"top\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.071823204419889%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.77900552486188%\" valign=\"top\"\u003e\n \u003cp\u003e614 (563)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.248618784530386%\" valign=\"top\"\u003e\n \u003cp\u003e576 (597)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.939226519337016%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2707182320442%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.7292817679558%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eStandardized Mean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eCI = Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eWelch Two Sample t-test; Pearson\u0026apos;s Chi-squared test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Labor duration, Trial of labor after cesarean section, Vaginal delivery, Cesarean section, Propensity scores, IPTW","lastPublishedDoi":"10.21203/rs.3.rs-3996134/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3996134/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction:\u003c/p\u003e\n\u003cp\u003eRepeat cesarean section (C-section) is associated with an increased risk of maternal complications and a reduction in quality of life (QOL). Trial of Labor After C-section (TOLAC) may reduce the recurrence of cesarean deliveries. Uterine rupture, a complication of TOLAC, is a serious obstetric complication, with prolonged labor duration suggested as a risk factor. However, there are no reports examining labor duration in women with cesarean scars, considering the termination of labor through cesarean section and selection bias. Our study aimed to investigate the impact of cesarean scars on the labor duration of TOLAC patients, considering these factors.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eFrom January 1, 2012, to December 31, 2021, we investigated a cohort of 2,964 individuals who attempted vaginal delivery at a single medical center, categorizing them into TOLAC and non-TOLAC groups. Propensity scores were generated based on factors that could influence labor duration, and survival analysis was conducted after applying Inverse Probability of Treatment Weighting (IPTW). \u0026nbsp;We evaluated the robustness of the results by propensity score matching and bootstrapping as a sensitivity analysis.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eResults from Cox proportional hazards regression analysis indicated that the unadjusted hazard ratio was 0.83 (95% Confidence Interval (CI): 0.70-0.98, p=0.027), whereas the IPTW-adjusted hazard ratio was 0.98 (95% CI: 0.74-1.30, p=0.91).\u003c/p\u003e\n\u003cp\u003eConclusion:\u003c/p\u003e\n\u003cp\u003eFactors such as maternal physique and fetal characteristics influence labor duration, with the impact of cesarean scars being limited. Our findings were consistent with previous studies and provided supportive evidence.\u003c/p\u003e","manuscriptTitle":"Evaluating the Impact of a Trial of Labor After Cesarean Section on Labor Duration: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 18:46:55","doi":"10.21203/rs.3.rs-3996134/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-25T09:21:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-24T19:08:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3c77cebf-b164-492b-8f39-88c69f98eb6a","date":"2024-03-14T02:52:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-12T10:40:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-12T10:35:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-06T08:50:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-06T08:48:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-02-28T08:35:26+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":"07bb26c4-2dae-409e-b130-7288f989765b","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:35:42+00:00","versionOfRecord":{"articleIdentity":"rs-3996134","link":"https://doi.org/10.1186/s12884-024-06744-0","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2024-08-15 15:58:26","publishedOnDateReadable":"August 15th, 2024"},"versionCreatedAt":"2024-03-08 18:46:55","video":"","vorDoi":"10.1186/s12884-024-06744-0","vorDoiUrl":"https://doi.org/10.1186/s12884-024-06744-0","workflowStages":[]},"version":"v1","identity":"rs-3996134","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3996134","identity":"rs-3996134","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}