Prediction models for caesarean section following induction of labour: A systematic review of methodology and reporting quality

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Our systematic review aimed to appraise the methodology and reporting quality of recently published studies that developed or externally validated prediction models for caesarean section at term following induction of labour. Methods : MEDLINE, Scopus, Embase, IEEE Xplore and CINAHL Complete databases were searched to identify original research studies published since 2017. Studies that did not report any performance measurement of the prediction model were excluded. No restrictions were applied to study designs, populations, modelling algorithms, type and timing of predictors, and methods of induction. Descriptive analysis was performed to assess heterogeneity across eligible studies. Study risk of bias and model applicability were assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Study reporting quality was evaluated using the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD+AI) Statement. Results : Among the 14 included studies, 12 developed a single model using logistic or Bayesian regression, and two externally validated included models. All studies reported discrimination performance using the area under the receiver operating characteristic (AUROC) curve, and 11 reported calibration performance. However, only one validation-only study assessed clinical utility using decision-curve analysis, and none evaluated model fairness. Overall, 12 studies had unclear or high risk of bias, mainly attributable to the selection of predictors through univariate analysis. Three studies raised applicability concerns due to the inclusion of predictors not available before induction. The handling of missing data was not reported in five studies. Conclusions : More external validation is needed to assess the clinical utility and fairness of existing models before they can be recommended for the subsequent stages in the clinical prediction pipeline and ultimately support clinical decision-making and improve maternal and neonatal outcomes. Future research should prioritise the inclusion of comprehensive performance measures and strengthen methodological rigour and reporting transparency to improve model reliability and clinical applicability, as emphasised in existing reporting guidelines. birth caesarean section clinical prediction model induction of labour TRIPOD + AI reporting quality Figures Figure 1 Figure 2 BACKGROUND Induction of labour (IOL) is a common obstetric procedure being performed to prevent adverse maternal and neonatal health outcomes that may occur at a later gestational age or in response to maternal request ( 1 ). This procedure is typically recommended only when there is a clear medical indication and the expected benefits outweigh the potential risks, compared with awaiting pregnancies to progress naturally (referred to as expectant management) or performing a pre-labour caesarean section (CS) ( 2 ). Previous epidemiological studies (both interventional and observational) have been extensively conducted to examine the associations between IOL and CS by estimating relative risks and identifying risk factors. These risk factors include primiparity ( 3 ), advanced maternal age (> 35 years) ( 4 ), obesity (body mass index (BMI) ≥ 30) ( 5 ), high infant birthweight (macrosomia) ( 3 ), post-term pregnancy (> 42 weeks of gestation) ( 3 ), and unfavourable cervix status (Bishop score < 6) ( 6 ). While these studies have provided valuable insights into the associations between IOL and CS, it is important to note that relative risks and risk factors alone are insufficient for accurately quantifying the personalised probability of CS for a woman considering IOL. Notably, not every individual risk factor will necessarily lead to an increased risk of adverse outcomes, and its impact can be influenced by the presence or absence of other factors. Furthermore, many epidemiological studies included risk factors that were known after or during the induction (e.g. methods of induction or timing of induction), which are not predictive factors for CS, before IOL is undertaken, and thus not useful for informing women and clinicians prior to the intervention. Prediction models are able to prognostically assess an individual woman’s absolute risk after weighing several predictive factors simultaneously. However, quality assessment is essential before promoting the possible clinical impact evaluation and pilot implementation studies. Our study aimed to summarise and appraise the methodology and reporting quality of recently published studies that developed or externally validated prediction models for term CS (37–42 weeks of gestation) following IOL (attempted for any reason). METHODS This study was reported based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist ( 7 ) (Appendix A & B) and conducted based on the relevant guideline for systematic reviews of prognostic prediction models ( 8 ). The protocol for the systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD42022301631). Eligibility criteria Studies were included if they (i) reported on prognostic predictions (either development or validation) for CS at term following IOL; (ii) reported performance measurement of the prediction model; (iii) published between 01/01/2017 and 24/04/2023; (iv) published in peer-reviewed journals or conferences; (v) published in English; (vi) presented primary research; and (vii) were applicable to a human population. Studies were excluded if they (i) did not report on predictions for CS at term (predict preterm CS only ( 42 weeks of gestation)); (ii) reported on predictions for CS not following IOL (pre-labour CS or CS following spontaneous onset of labour (either with or without augmentation/acceleration of labour); (iii) reported on predictions for health outcomes other than CS (e.g. vaginal birth, preeclampsia, or postpartum haemorrhage); (iv) did not report on prognostic predictions (e.g. explored the association between factors and CS, or classified women into arbitrary risk categories (e.g. low risk vs. high risk) without providing the absolute probability of CS; (v) did not report performance measurement of the prediction model; (vi) published before 01/01/2017 or beyond 24/04/2023; (vii) did not publish in peer-reviewed journals or conferences; (viii) published in languages other than English; (ix) presented secondary (reviews) or tertiary (reviews of reviews) research, or grey literature; (xi) published as an abstract or comment; or (xi) were not applicable to a human population. No restrictions were applied to study designs, populations, modelling algorithms, methods of induction, type and timing of predictors, and reasons for CS (e.g. failure to progress). Information sources Five electronic databases were searched on 24th April 2023: MEDLINE (via Ovid), Scopus (via Elsevier), Embase (via Ovid), IEEE Xplore, and CINAHL Complete (via EBSCOhost). The reference lists of the included studies were hand-searched for related articles. Search strategy The search terms used were revolved around the central concepts: (i) maternal health care; (ii) risk predictions; and (iii) algorithms. Searches incorporated the use of Medical Subject Headings (MeSH), Boolean operators (AND, OR) and proximity searching. The search strategies used for each database were available in Appendix C. Study selection Search results were imported into Covidence and duplicates were removed. One reviewer (YH) undertook title and abstract screening and full-text review, with a random 20% checked by a second reviewer (XZ/SG). If the agreement was less than 98%, then another 20% was checked, and so forth. The reasons for exclusion were categorised at the full-text review stage. Discrepancies during the title and abstract screening and full-text review were discussed by the two reviewers (YH and XZ/SG) until a consensus was reached. The researchers were blinded to each other’s decisions in both title and abstract screening and full-text review. A PRISMA flow diagram illustrating the process of study selection is shown in Fig. 1 . Data extraction The data extraction was performed by one reviewer (YH) and checked by a second reviewer (XZ) in Covidence. We extracted data from included studies guided by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) ( 9 ) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD + AI) Statement ( 10 ). Quality assessment Study quality was assessed by one reviewer (YH) and checked by a second reviewer (XZ) in Covidence using the Prediction model study Risk of Bias Assessment Tool (PROBAST) ( 11 ), which is a tool to assess the risk of bias and concern in the applicability for the systematic review of the prognostic prediction models. Reporting quality was evaluated TRIPOD + AI Statement ( 10 ). Data synthesis The data extracted from the eligible studies and their results of quality appraisal were summarised in tables using Microsoft Excel. Descriptive analysis was performed to assess the heterogeneity of modelling methods, study designs, type of validation, type and timing of predictors, population characteristics, performance measurement metrics, and risk of bias across eligible studies. For studies that reported multiple outcomes (e.g. preterm CS and term CS), only term CS was included for analysis in our review. For studies that conducted multiple analyses (sensitivity analyses or subgroup analyses), only primary analyses were included in our review. For validation-only studies, only the performance of published models included in our review was extracted for analysis. For discrimination measurement, effect sizes and their 95% confidence interval (CI) were presented for each dataset. For calibration measurement, R 2 of the fitted line and p-value of the Hosmer-Lemeshow test were reported. For classification measurement, effect sizes were reported as percentages. For studies that used multiple cut-off points, only the values of the first cut-off point were reported. Discrepancies in the terminology The definition of candidate predictors, predictors, and validation varies across eligible studies. For our review, we defined candidate predictors as variables included for selection during multivariable modelling, predictors/features as variables included in the final model, and external validation as studies validated their prediction models either using the same dataset with the model development but in separate populations from different hospitals (geographical) or different timeframe (temporal), or using a completely different dataset (independent). For studies that randomly split the development datasets into two/three, validation based on the ‘testing’ dataset was considered as internal validation. RESULTS Study selection This study extracted 27,052 records from five electronic literature databases and 14 records from hand-searching the reference lists of the eligible studies. After duplicate removal, screening, and eligibility assessment, 14 studies were included in our review (Fig. 1 ). A list of studies excluded during full-text review and reasons for exclusion is provided in Table S6. Study characteristics Nine studies were conducted in high-income countries (five in the United States ( 12 – 16 ), two in Spain ( 17 , 18 ), and two in France ( 19 , 20 )). Others were conducted in China ( 21 , 22 ), Iran ( 23 ), Turkey ( 24 ), Egypt and Italy ( 25 ) (Table 1 ). Table 1 Summary characteristics of eligible studies Study characteristic N, % Country the study was conducted (n = 14) The United States 5, 35.7% Spain 2, 14.3% China 2, 14.3% France 2, 14.3% Iran 1, 7.1% Turkey 1, 7.1% Egypt and Italy 1, 7.1% Study design of training dataset for development study or validation dataset for validation-only study (n = 14) Retrospective cohort 6, 42.9% Prospective cohort 7, 50% Randomised controlled trial 1, 7.1% Populations of training dataset for development studies (n = 12) Total sample size, median (range) 809 (200–4,177,644) Caesarean section rate, median (range) 20.7% (7.4–40.9%) Gestational age, range 23–44 Women with unfavourable cervix status only 6, 42.9% Nulliparous women only 2, 16.7% Obese women only 1, 8.3% Women without medical indications of induction only 1, 8.3% Pregnancies with small-for-gestational-age fetus 1, 8.3% Predictors for development study (n = 12) Number of predictors in the final model, median (range) 7 ( 3 – 11 ) Caesarean section per candidate predictor in training dataset, median (range) 27.4 (4.6–24,255.2) Type of algorithm for development study (n = 12) Standard logistic regression 10, 83.3% Penalised logistic regression 1, 8.3% Bayesian regression 1, 8.3% Performance measurement (n = 14) Discrimination 14, 100% Area Under the Receiver Operating Characteristic curve (AUROC) 14, 100% AUROC of internal validation for development study, median (range) 0.78 (0.71–0.88) AUROC of external validation for development study, median (range) 0.78 (0.67–0.82) Classification 8, 57.1% Sensitivity 8, 57.1% Specificity 8, 57.1% Positive predictive value 6, 42.9% Negative predictive value 6, 42.9% Calibration 11, 78.6% Calibration plot 7, 50% Hosmer-Lemeshow test 4, 28.9% Clinical utility by decision curve analysis 1, 7.1% Model accessibility for development studies (n = 12) Web-based platform 3, 25% Nomogram 4, 33.3% Type of validation for development studies (n = 12) Internal 11, 91.7% Random split 4, 33.3% Bootstrapping 4, 33.3% Cross-validation 3, 25% External 6, 50% Temporal split 3, 25% Geographical split 1, 8.3% Independent 2, 16.7% Risk of bias (n = 14) Participants Low 14, 100% Predictors Low 11, 78.6% High 3, 21.4% Outcome Low 11, 78.6% High 3, 21.4% Analysis Low 2, 14.3% Unclear 5, 35.7% High 7, 50% Overall Low 2, 14.3% Unclear 5, 35.7% High 7, 50% For development datasets, 11 were cohort studies (six retrospective ( 12 , 14 – 16 , 21 , 24 ), and five prospective ( 17 , 19 , 22 , 23 , 25 )). One study was conducted following a randomised clinical trial and validated using a retrospective cohort ( 13 ), and another study used a prospective cohort for development and a retrospective cohort for validation ( 19 ) (Table 1 ). Two validation studies both used prospective cohorts ( 18 , 20 ). Four studies conducted population-level analyses ( 15 , 16 , 19 , 20 ) (Table S1 ). Modelling methods We included 12 development studies, all developed a single prediction model, 10 using traditional logistic regression ( 12 – 17 , 21 – 23 , 25 ), one applying penalised logistic regression with maximum likelihood estimation ( 24 ), and one using Bayesian regression ( 19 ) (Table 1 ). Type of validation Lu 2020 ( 22 ) developed a model without validation. The other 11 internally validated their models using either bootstrapping ( 12 , 14 , 21 , 24 ), cross-validation ( 15 , 16 , 25 ), random split ( 17 , 19 , 23 , 25 ) techniques, or an observational cohort from the same institution during the trial period ( 13 ) (Table 1 ). Six development studies further externally assessed their models on separate populations who were different from the training cohort: three conducted temporal validation ( 15 , 16 , 21 ), one used geographical validation ( 12 ), and two performed independent validation (one within the same setting ( 13 ) and another in different setting ( 19 )) (Table 1 ). One validation-only study validated Levine 2018 (the United States), Migliorelli 2019 (Spain), and Rossi 2020 (the United States) models in the Spanish context ( 18 ). Another validation-only study validated Levine 2018 in the French context ( 20 ). Two development studies also validated Levine’s 2018 model in the United States ( 19 ) and Rossi’s 2020 model in China ( 21 ). Populations The median sample size for training datasets of 12 development studies was 809 and ranged from 200 ( 23 ) to 4,177,644 ( 15 ), and the median rate of CS in the training datasets was 20.7% and ranged from 7.4% ( 12 ) to 40.9% ( 14 ) (Table 1 ). Six studies included women with unfavourable cervix status only ( 13 , 19 – 21 , 23 , 24 ), two studies included nulliparous women only ( 21 , 25 ), one study focused on obese women ( 16 ), one study focused on pregnancies with small-for-gestational-age fetus ( 14 ), and one study focused on women without medical indications of induction ( 12 ) (Table 1 ). Outcome of interest All eligible studies developed/validated a prediction model for CS following IOL, with one study focused on one type of induction method (Dinoprostone insertion) ( 24 ), two studies only included women who underwent cervical ripening ( 19 , 20 ), and one study only predicted CS for failure to progress ( 25 ). Model accessibility All development studies provided a formula for their final models, with four studies further presented as nomograms ( 13 , 21 , 23 , 24 ) and three studies presented as interactive web-based applications ( 13 , 15 , 16 ). Predictors in the final model The median number of predictors included in the final model was seven and ranged from three ( 14 ) to 11 ( 19 ) (Table 1 ). A total of 38 unique predictors were included in the final models, with 22 appearing only once (Fig. 2 ). Common predictors included in the final models were maternal height (n = 8) ( 13 , 15 – 17 , 19 , 21 – 23 ), maternal age (n = 7) ( 12 , 14 – 16 , 21 , 24 , 25 ), parity (n = 7) ( 12 , 13 , 16 , 19 , 23 , 24 ), and gestational age at birth/induction (n = 7) ( 12 – 15 , 19 , 23 , 24 ). Three studies included ultrasound assessments as predictors ( 17 , 22 , 25 ). Maternal age was treated as a continuous variable only. Three studies categorised BMI ( 13 , 19 , 23 ), and four studies categorised maternal height ( 13 , 19 , 22 , 23 ) (Table S3). In terms of the direction of these predictor factors, several studies reported a higher probability of CS following IOL for shorter maternal height ( 13 , 15 – 17 , 19 , 21 , 22 ), older maternal age ( 12 , 14 – 16 , 21 , 25 ), nulliparity ( 12 , 13 , 16 , 19 , 22 – 24 ), higher BMI or maternal weight, or obesity measured at different time points ( 12 , 13 , 15 – 17 , 19 ), later gestational age ( 12 , 13 , 19 , 23 ), and lower Bishop score (either modified or not) ( 13 , 21 , 23 , 24 ). Interestingly, one study ( 23 ) included the predicted probability of CS using a previously published model as a predictor ( 13 ) in its model. Performance measurement Five development studies and one validation-only study evaluated their performance using all calibration, discrimination, and classification measurements ( 12 , 14 – 18 ) (Table S1 ). Only one validation-only study assessed clinical utility using decision-curve analysis ( 18 ), and none evaluated fairness. The Area Under the Receiver Operating Characteristic curve (AUROC) was reported in all eligible studies, ranging from 0.71 ( 23 ) to 0.88 ( 25 ) for internal validations, and 0.67 ( 21 ) to 0.82 ( 12 ) for external validations in 12 development studies (Table 1 ). Calibration was assessed in 11 studies using the Hosmer–Lemeshow test ( 12 , 14 , 17 , 18 ) or calibration plots/curves ( 12 , 13 , 15 , 16 , 19 , 21 , 24 ) (Table S4). Quality assessment Overall, two studies (one development and one validation-only) were classified as having a low risk of bias ( 12 , 18 ), and 11 had a low concern of applicability ( 12 , 13 , 15 – 18 , 20 – 23 , 25 ) (Table 2 ). Three studies included predictors available after the start of induction ( 14 , 19 , 24 ) (Fig. 2 ), which raised a high concern about their applicability to our review question, which is to prognostically predict the risk of CS before the start of induction. The domain of analysis was identified as the main contributor to the high risk of bias in our review (Table 1 ). Eligible studies predominantly selected candidate predictors through univariate analysis prior to conducting multivariable modelling, indicating a potential risk of bias in the analysis process ( 13 – 16 , 19 , 21 , 22 , 25 ) (Table S5). Events per variable (EPV) of one study was less than 10 ( 17 ), and of four studies was less than 20 ( 13 , 22 , 23 , 25 ), indicating that there was not a reasonable number of populations with the outcome of interest. The handling of missing data was not reported in five studies ( 13 , 15 , 19 , 22 , 24 ). Table 2: Risk of bias assessment of eligible studies Table 3 Characteristics of externally validated models Validated Model (Author Year Country Ref.) Study ID (Author Year Country) Total population Caesarean section, % AUROC 95% CI Calibration Classification Clinical utility Type Risk of bias Danilack 2020 USA ( 12 ) Danilack 2020 USA 17,370 1,282, 7.4% 0.82 0.81–0.83 ☑ ☑ N/R Training Low Danilack 2020 USA 2,122 154, 7.3% 0.82 0.79–0.86 ☑ ☑ N/R Validation Jochum 2019 France ( 19 ) Jochum 2019 France 1,024 250, 24.4% 0.76 0.73–0.79 ☑ N/R N/R Training High Jochum 2019 USA* 4,242 790, 18.6% 0.81 0.79–0.82 ☑ N/R N/R Validation Levine 2018 USA ( 13 ) Levine 2018 USA 491 136, 27.7% 0.79 0.74–0.83 ☑ N/R N/R Training Unclear Levine 2018 USA 8,466 2,235, 26.4% 0.73 0.72–0.74 ☑ N/R N/R Validation Jochum 2019 USA 4,242 790, 18.6% 0.76 0.08–0.78 N/R N/R N/R Validation López-Jiménez 2022 Spain ( 18 ) 468 148, 31.6% 0.77 0.72–0.83 ☑ ☑ ☑ Validation Low Tollon 2023 France ( 20 ) 600 192, 32% 0.69 0.65–0.74 ☑ N/R N/R Validation Unclear Migliorelli 2019 Spain ( 14 ) Migliorelli 2019 Spain 338 70, 20.7% 0.83 0.78–0.87 ☑ ☑ N/R Training High López-Jiménez 2022 Spain ( 18 ) 328 99, 30.2% 0.74 0.68–0.79 ☑ ☑ N/R Validation Low Rossi 2019 USA ( 16 ) Rossi 2019 USA 1,098,981 273,184, 24.9% 0.79 0.78–0.79 ☑ ☑ N/R Training High Rossi 2019 USA 197,982 48,881, 24.7% 0.77 0.76–0.77 ☑ ☑ N/R Validation Rossi 2020 USA ( 15 ) Rossi 2020 USA 4,177,644 800,423, 19.2% 0.79 0.79–0.79 ☑ ☑ N/R Training Unclear Rossi 2020 USA 940,383 N/R 0.78 0.76–0.80 ☑ ☑ N/R Validation López-Jiménez 2022 Spain ( 18 ) 468 148, 31.6% 0.75 0.71–0.80 ☑ ☑ ☑ Validation Low Zhou 2022 China 1,935 317, 16.4% 0.64 N/R N/R N/R N/R Validation Unclear Zhou 2022 China ( 21 ) Zhou 2022 China 2,950 392, 13.3% 0.73 0.70–0.75 ☑ ☑ N/R Training Zhou 2022 China 1,935 317, 16.4% 0.67 0.64–0.70 ☑ ☑ N/R Validation Abbreviations: N/R = Not Reported; AUROC = Area Under the Receiver Operating Characteristic curve; CI = Confidence Interval. *The same study used a USA cohort for validation. ☑ = Reported. DISCUSSION Main Findings In total, 14 studies (12 development and two validation-only) published between 2017 and 2023 were included in our systematic review. All development studies used statistical methods (logistic or Bayesian regression), 11 models were internally validated, and seven were also externally validated. All studies reported discrimination performance using AUROC, 11 reported calibration performance, only one validation-only study assessed clinical utility using decision-curve analysis, and none evaluated fairness. Overall, 12 studies had unclear or high risk of bias, mainly attributable to the selection of predictors through univariate analysis. Three studies raised applicability concerns due to the inclusion of predictors not available before induction. Strengths and Limitations This is the first review to use the TRIPOD + AI and PROBAST criteria to assess the risk of bias, model applicability, and reporting quality of prediction models for term CS following IOL. The identification of low reporting quality, high risk of bias, and high concern of model applicability in the existing models can guide researchers in overcoming these limitations and developing more robust, reliable, and generalised prediction models. We originally aimed to conduct a meta-analysis comparing model performance between statistical and machine learning approaches. However, as all included studies employed statistical methods, such an analysis was not feasible. Comparison with existing literature Our findings echo previous systematic reviews of prediction models for maternal outcomes, revealing widespread methodological weaknesses and poor reporting ( 18 , 26 ). Particularly, the high risk of bias due to the use of univariate analysis for candidate predictor selection and the presence of a low EPV ratio. Common predictors identified in our review for higher CS following IOL were shorter maternal height, higher maternal weight, older maternal age, nulliparity, low Bishop score, and later gestational age at birth/induction, which aligned with previous studies identifying risk factors associated with CS following IOL ( 3 – 5 , 27 , 28 ). It is important to acknowledge that the cervical status of women can change rapidly in clinical practice (time-varying variable). Therefore, future research should clearly specify the timing of measurement to account for these dynamic changes and ensure the accuracy and applicability of predictive models in clinical practice. Clinical implications Clinical utility refers to the consequences of decisions made based on the output of a prediction model and is distinct from discrimination and calibration measurements. It is commonly assessed using decision curve analysis, which estimates the net benefit of applying the model across a range of threshold probabilities ( 29 ). Net benefit quantifies the trade-off between the benefit of correctly predicting true positives and the harm of incorrectly predicting false positives. A clinically reasonable range of threshold probabilities can be determined by considering the maximum number of false positives that health care providers and systems are generally willing to tolerate in order to correctly identify one true positive, and it critically depends on the clinical context. In most cases, this threshold does not exceed 50% as missing a true positive is generally considered more harmful than a false positive case ( 30 ). In our review, only one validation-only study examined the clinical utility of two included models (Levin 2018 and Rossi 2020) ( 18 ). However, their interpretation of the results was inappropriate as they reversed the proper sequence between threshold probability and model evaluation. Specifically, the decision curve results should not be used to set the range of threshold probability for the model. Instead, a clinically reasonable range of threshold probabilities should be defined first, and then used to evaluate which model provides the highest net benefit across that range ( 29 ). To date, no such threshold range has been determined for IOL, warranting future qualitative research. CONCLUSIONS Although the included prediction models for term CS following IOL exhibited acceptable or excellent discrimination, most are subject to methodological limitations (e.g. univariate analysis for candidate predictor selection or inclusion of predictors known after or during induction). Clinical utility was rarely assessed, and none of the studies evaluated model fairness. Future research should prioritise the inclusion of comprehensive performance measures and strengthen methodological rigour and reporting transparency to improve model reliability and clinical applicability, as emphasised in existing reporting guidelines. ABBREVIATIONS Area Under the Receiver Operating Characteristic curve (AUROC) Body Mass Index (BMI) Caesarean Section (CS) Confidence Interval (CI) Induction Of Labour (IOL) Prospective Register Of Systematic Reviews (PROSPERO) Prediction model Risk Of Bias ASsessment Tool (PROBAST) Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD+AI) Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and materials: Alldata generated or analysed during this study are included in this published article and its supplementary files. Competing interests: No competing interests. Funding: Emily Callander receives salary support from the National Health and Medical Research Council (NHMRC) through fellowship schemes. Yanan HU receives support from the Australian Government Research Training Program (RTP) Scholarship. The funders were not involved in the preparation of this study. Authors’ contributions: Yanan Hu led the study conception and design, development of the search strategy, database searching, screening and selection of studies, data extraction and synthesis, interpretation of the findings, and drafting of the manuscript. Xin Zhang screened titles and abstracts, reviewed full-text articles, and checked the results of data extraction and quality appraisal. Swapna Gokhale screened titles and abstracts. 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Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. Wolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Annals of internal medicine. 2019;170(1):51-8. Danilack VA, Hutcheon JA, Triche EW, Dore DD, Muri JH, Phipps MG, et al. Development and Validation of a Risk Prediction Model for Cesarean Delivery after Labor Induction. Journal of Women's Health. 2020;29(5):656-69. Levine LD, Downes KL, Parry S, Elovitz MA, Sammel MD, Srinivas SK. A validated calculator to estimate risk of cesarean after an induction of labor with an unfavorable cervix. American Journal of Obstetrics & Gynecology. 2018;218(2):254.e1-.e7. Nwabuobi C, Gowda N, Schmitz J, Wood N, Pargas A, Bagiardi L, et al. 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Risk of caesarean delivery in labour induction: a systematic review and external validation of predictive models. BJOG. 2022;129(5):685-95. Jochum F, Le Ray C, Blanc-Petitjean P, Langer B, Meyer N, Severac F, et al. Externally Validated Score to Predict Cesarean Delivery After Labor Induction With Cervi Ripening. Obstetrics & Gynecology. 2019;134(3):502-10. Tollon P, Blanc-Petitjean P, Drumez E, Ghesquière L, Le Ray C, Garabedian C. Prediction of successful labor induction with very unfavorable cervix: A comparison of six scores. International Journal of Gynecology & Obstetrics. 2023;160(1):53-8. Zhou H, Gu N, Yang Y, Wang Z, Hu Y, Dai Y. Nomogram predicting cesarean delivery undergoing induction of labor among high-risk nulliparous women at term: a retrospective study. BMC Pregnancy and Childbirth. 2022;22(1):55. Lu J, Cheng YKY, Ho SYS, Sahota DS, Hui LL, Poon LC, et al. The predictive value of cervical shear wave elastography in the outcome of labor induction. Acta Obstet Gynecol Scand. 2020;99(1):59-68. Hemmatzadeh S, Abbasalizadeh F, Mohammad-Alizadeh-Charandabi S, Asghari Jafarabadi M, Mirghafourvand M. Development and Validation of a Nomogram to Estimate Risk of Cesarean After Induction of Labor in Term Pregnancies with an Unfavorable Cervix in Iran. Clinical nursing research. 2022;31(7):1332-9. Bademkiran MH, Bademkiran C, Ege S, Peker N, Sucu S, Obut M, et al. Explanatory variables and nomogram of a clinical prediction model to estimate the risk of caesarean section after term induction. Journal of Obstetrics and Gynaecology. 2021;41(3):367-73. Kamel RA, Negm SM, Youssef A, Bianchini L, Brunelli E, Pilu G, et al. Predicting cesarean delivery for failure to progress as an outcome of labor induction in term singleton pregnancy. American Journal of Obstetrics & Gynecology. 2021;224(6):609.e1-.e11. Meier K, Parrish J, D'Souza R. Prediction models for determining the success of labor induction: A systematic review. Acta Obstet Gynecol Scand. 2019;98(9):1100-12. Tolcher MC, Holbert MR, Weaver AL, McGree ME, Olson JE, El-Nashar SA, et al. Predicting Cesarean Delivery After Induction of Labor Among Nulliparous Women at Term. Obstetrics & Gynecology. 2015;126(5):1059-68. Quach D, ten Eikelder M, Jozwiak M, Davies-Tuck M, Bloemenkamp KWM, Mol BW, et al. Maternal and fetal characteristics for predicting risk of Cesarean section following induction of labor: pooled analysis of PROBAAT trials. Ultrasound in Obstetrics & Gynecology. 2022;59(1):83-92. Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagnostic and Prognostic Research. 2019;3(1):18. Wynants L, van Smeden M, McLernon DJ, Timmerman D, Steyerberg EW, Van Calster B. Three myths about risk thresholds for prediction models. BMC Med. 2019;17(1):192. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in BMC Medical Research Methodology → Version 1 posted Editorial decision: Revision requested 30 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor invited by journal 13 Aug, 2025 Editor assigned by journal 12 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 08 Aug, 2025 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. 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HU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACgwNg6gADPwOUwcCQQKQWyQZitcBVwvUS1MLPfvbg5wKGO4mbrx0+9uDHnztAkRwDvFrYePKSpWcwPEvcdjst3bC37RmDZM8bAloYcgykeRgOA7XkmEnwNhxmMLhByBb+N8a/QVo2z84xk/zz5zCDPUEtEjlmYFs2SIMYbEBbJAhqeWNmzWNw2HjG7bQ0adm2wzwSZ54VEHBYjvFtnorDsv2zk49JvvlzWI6/PXkDXi0QgOQSHiKUj4JRMApGwSggBADRnkeefPgvwgAAAABJRU5ErkJggg==","orcid":"","institution":"Monash University","correspondingAuthor":true,"prefix":"","firstName":"Yanan","middleName":"","lastName":"HU","suffix":""},{"id":512922643,"identity":"6b565cab-2117-448e-b4e9-066072d13ba8","order_by":1,"name":"Xin ZHANG","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"ZHANG","suffix":""},{"id":512922644,"identity":"21dfe9e7-8a6b-431e-98f2-d3c160f2bc32","order_by":2,"name":"Swapna GOKHALE","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Swapna","middleName":"","lastName":"GOKHALE","suffix":""},{"id":512922645,"identity":"20f4c45e-4d5c-4e21-985b-ca17ae177ee7","order_by":3,"name":"Valerie SLAVIN","email":"","orcid":"","institution":"Gold Coast University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Valerie","middleName":"","lastName":"SLAVIN","suffix":""},{"id":512922646,"identity":"cbe02533-8f30-459a-aace-87390dd024ec","order_by":4,"name":"Joanne ENTICOTT","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Joanne","middleName":"","lastName":"ENTICOTT","suffix":""},{"id":512922647,"identity":"006a31d9-6c54-4698-a7aa-7e9bf8358dd9","order_by":5,"name":"Emily CALLANDER","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"CALLANDER","suffix":""}],"badges":[],"createdAt":"2025-08-09 03:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7331090/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7331090/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12874-026-02767-7","type":"published","date":"2026-01-27T15:59:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91121679,"identity":"f8b1f4cb-3f66-422d-83a0-220d4120817e","added_by":"auto","created_at":"2025-09-11 19:10:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA PRISMA flow diagram of eligible studies\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7331090/v1/d73316efbb697d4e872ed451.png"},{"id":91123180,"identity":"a9009bdc-e83c-4da6-9227-e00de8efce5e","added_by":"auto","created_at":"2025-09-11 19:34:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141690,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA bubble chart of predictor frequencies in the final models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRed bubbles indicate factors that are typically assessed immediately before or during induction (e.g., cervical dilation, Bishop score), therefore are not available early enough to support induction planning in advance.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7331090/v1/e433ed0561c534420156b2e3.png"},{"id":101690839,"identity":"0d3017c9-1f35-4732-b45a-9552de945885","added_by":"auto","created_at":"2026-02-02 16:09:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1656277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7331090/v1/c943a36c-6f3b-4244-a031-f2464ee83492.pdf"},{"id":91122322,"identity":"8c0c4bf9-1be8-4c93-8320-001bd3c80f43","added_by":"auto","created_at":"2025-09-11 19:18:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":157131,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7331090/v1/ae577398df32c0d7e07ac7f3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction models for caesarean section following induction of labour: A systematic review of methodology and reporting quality","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eInduction of labour (IOL) is a common obstetric procedure being performed to prevent adverse maternal and neonatal health outcomes that may occur at a later gestational age or in response to maternal request (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This procedure is typically recommended only when there is a clear medical indication and the expected benefits outweigh the potential risks, compared with awaiting pregnancies to progress naturally (referred to as expectant management) or performing a pre-labour caesarean section (CS) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious epidemiological studies (both interventional and observational) have been extensively conducted to examine the associations between IOL and CS by estimating relative risks and identifying risk factors. These risk factors include primiparity (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), advanced maternal age (\u0026gt;\u0026thinsp;35 years) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), obesity (body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), high infant birthweight (macrosomia) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), post-term pregnancy (\u0026gt;\u0026thinsp;42 weeks of gestation) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and unfavourable cervix status (Bishop score\u0026thinsp;\u0026lt;\u0026thinsp;6) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While these studies have provided valuable insights into the associations between IOL and CS, it is important to note that relative risks and risk factors alone are insufficient for accurately quantifying the personalised probability of CS for a woman considering IOL. Notably, not every individual risk factor will necessarily lead to an increased risk of adverse outcomes, and its impact can be influenced by the presence or absence of other factors. Furthermore, many epidemiological studies included risk factors that were known after or during the induction (e.g. methods of induction or timing of induction), which are not predictive factors for CS, before IOL is undertaken, and thus not useful for informing women and clinicians prior to the intervention.\u003c/p\u003e\u003cp\u003ePrediction models are able to prognostically assess an individual woman\u0026rsquo;s absolute risk after weighing several predictive factors simultaneously. However, quality assessment is essential before promoting the possible clinical impact evaluation and pilot implementation studies. Our study aimed to summarise and appraise the methodology and reporting quality of recently published studies that developed or externally validated prediction models for term CS (37\u0026ndash;42 weeks of gestation) following IOL (attempted for any reason).\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study was reported based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (Appendix A \u0026amp; B) and conducted based on the relevant guideline for systematic reviews of prognostic prediction models (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The protocol for the systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD42022301631).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEligibility criteria\u003c/h2\u003e\u003cp\u003eStudies were included if they (i) reported on prognostic predictions (either development or validation) for CS at term following IOL; (ii) reported performance measurement of the prediction model; (iii) published between 01/01/2017 and 24/04/2023; (iv) published in peer-reviewed journals or conferences; (v) published in English; (vi) presented primary research; and (vii) were applicable to a human population.\u003c/p\u003e\u003cp\u003eStudies were excluded if they (i) did not report on predictions for CS at term (predict preterm CS only (\u0026lt;\u0026thinsp;37 weeks of gestation) or post-term only (\u0026gt;\u0026thinsp;42 weeks of gestation)); (ii) reported on predictions for CS not following IOL (pre-labour CS or CS following spontaneous onset of labour (either with or without augmentation/acceleration of labour); (iii) reported on predictions for health outcomes other than CS (e.g. vaginal birth, preeclampsia, or postpartum haemorrhage); (iv) did not report on prognostic predictions (e.g. explored the association between factors and CS, or classified women into arbitrary risk categories (e.g. low risk vs. high risk) without providing the absolute probability of CS; (v) did not report performance measurement of the prediction model; (vi) published before 01/01/2017 or beyond 24/04/2023; (vii) did not publish in peer-reviewed journals or conferences; (viii) published in languages other than English; (ix) presented secondary (reviews) or tertiary (reviews of reviews) research, or grey literature; (xi) published as an abstract or comment; or (xi) were not applicable to a human population.\u003c/p\u003e\u003cp\u003eNo restrictions were applied to study designs, populations, modelling algorithms, methods of induction, type and timing of predictors, and reasons for CS (e.g. failure to progress).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInformation sources\u003c/h3\u003e\n\u003cp\u003eFive electronic databases were searched on 24th April 2023: MEDLINE (via Ovid), Scopus (via Elsevier), Embase (via Ovid), IEEE Xplore, and CINAHL Complete (via EBSCOhost). The reference lists of the included studies were hand-searched for related articles.\u003c/p\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eThe search terms used were revolved around the central concepts: (i) maternal health care; (ii) risk predictions; and (iii) algorithms. Searches incorporated the use of Medical Subject Headings (MeSH), Boolean operators (AND, OR) and proximity searching. The search strategies used for each database were available in Appendix C.\u003c/p\u003e\n\u003ch3\u003eStudy selection\u003c/h3\u003e\n\u003cp\u003eSearch results were imported into Covidence and duplicates were removed. One reviewer (YH) undertook title and abstract screening and full-text review, with a random 20% checked by a second reviewer (XZ/SG). If the agreement was less than 98%, then another 20% was checked, and so forth. The reasons for exclusion were categorised at the full-text review stage. Discrepancies during the title and abstract screening and full-text review were discussed by the two reviewers (YH and XZ/SG) until a consensus was reached. The researchers were blinded to each other\u0026rsquo;s decisions in both title and abstract screening and full-text review. A PRISMA flow diagram illustrating the process of study selection is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eThe data extraction was performed by one reviewer (YH) and checked by a second reviewer (XZ) in Covidence. We extracted data from included studies guided by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD\u0026thinsp;+\u0026thinsp;AI) Statement (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eQuality assessment\u003c/h2\u003e\u003cp\u003eStudy quality was assessed by one reviewer (YH) and checked by a second reviewer (XZ) in Covidence using the Prediction model study Risk of Bias Assessment Tool (PROBAST) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), which is a tool to assess the risk of bias and concern in the applicability for the systematic review of the prognostic prediction models. Reporting quality was evaluated TRIPOD\u0026thinsp;+\u0026thinsp;AI Statement (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData synthesis\u003c/h3\u003e\n\u003cp\u003eThe data extracted from the eligible studies and their results of quality appraisal were summarised in tables using Microsoft Excel. Descriptive analysis was performed to assess the heterogeneity of modelling methods, study designs, type of validation, type and timing of predictors, population characteristics, performance measurement metrics, and risk of bias across eligible studies. For studies that reported multiple outcomes (e.g. preterm CS and term CS), only term CS was included for analysis in our review. For studies that conducted multiple analyses (sensitivity analyses or subgroup analyses), only primary analyses were included in our review. For validation-only studies, only the performance of published models included in our review was extracted for analysis. For discrimination measurement, effect sizes and their 95% confidence interval (CI) were presented for each dataset. For calibration measurement, R\u003csup\u003e2\u003c/sup\u003e of the fitted line and p-value of the Hosmer-Lemeshow test were reported. For classification measurement, effect sizes were reported as percentages. For studies that used multiple cut-off points, only the values of the first cut-off point were reported.\u003c/p\u003e\n\u003ch3\u003eDiscrepancies in the terminology\u003c/h3\u003e\n\u003cp\u003eThe definition of candidate predictors, predictors, and validation varies across eligible studies. For our review, we defined candidate predictors as variables included for selection during multivariable modelling, predictors/features as variables included in the final model, and external validation as studies validated their prediction models either using the same dataset with the model development but in separate populations from different hospitals (geographical) or different timeframe (temporal), or using a completely different dataset (independent). For studies that randomly split the development datasets into two/three, validation based on the \u0026lsquo;testing\u0026rsquo; dataset was considered as internal validation.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy selection\u003c/h2\u003e\n \u003cp\u003eThis study extracted 27,052 records from five electronic literature databases and 14 records from hand-searching the reference lists of the eligible studies. After duplicate removal, screening, and eligibility assessment, 14 studies were included in our review (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A list of studies excluded during full-text review and reasons for exclusion is provided in Table S6.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy characteristics\u003c/h2\u003e\n \u003cp\u003eNine studies were conducted in high-income countries (five in the United States (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e), two in Spain (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e), and two in France (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e)). Others were conducted in China (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e), Iran (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e), Turkey (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), Egypt and Italy (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary characteristics of eligible studies\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy characteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN, %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eCountry the study was conducted (n\u0026thinsp;=\u0026thinsp;14)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe United States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5, 35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEgypt and Italy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eStudy design of training dataset for development study or validation dataset for validation-only study (n\u0026thinsp;=\u0026thinsp;14)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetrospective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6, 42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7, 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandomised controlled trial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePopulations of training dataset for development studies (n\u0026thinsp;=\u0026thinsp;12)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal sample size, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e809 (200\u0026ndash;4,177,644)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaesarean section rate, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.7% (7.4\u0026ndash;40.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGestational age, range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen with unfavourable cervix status only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6, 42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNulliparous women only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese women only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWomen without medical indications of induction only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregnancies with small-for-gestational-age fetus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePredictors for development study (n\u0026thinsp;=\u0026thinsp;12)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of predictors in the final model, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaesarean section per candidate predictor in training dataset, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.4 (4.6\u0026ndash;24,255.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eType of algorithm for development study (n\u0026thinsp;=\u0026thinsp;12)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStandard logistic regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10, 83.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePenalised logistic regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayesian regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ePerformance measurement (n\u0026thinsp;=\u0026thinsp;14)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrimination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14, 100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea Under the Receiver Operating Characteristic curve (AUROC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14, 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUROC of internal validation for development study, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.71\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUROC of external validation for development study, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.67\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8, 57.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8, 57.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8, 57.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6, 42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6, 42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalibration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11, 78.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalibration plot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7, 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHosmer-Lemeshow test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4, 28.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical utility by decision curve analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1, 7.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eModel accessibility for development studies (n\u0026thinsp;=\u0026thinsp;12)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeb-based platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3, 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNomogram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4, 33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eType of validation for development studies (n\u0026thinsp;=\u0026thinsp;12)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11, 91.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom split\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4, 33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBootstrapping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4, 33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCross-validation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3, 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6, 50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemporal split\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3, 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeographical split\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1, 8.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eRisk of bias (n\u0026thinsp;=\u0026thinsp;14)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14, 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11, 78.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3, 21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11, 78.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3, 21.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5, 35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7, 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2, 14.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5, 35.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7, 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFor development datasets, 11 were cohort studies (six retrospective (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), and five prospective (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e)). One study was conducted following a randomised clinical trial and validated using a retrospective cohort (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e), and another study used a prospective cohort for development and a retrospective cohort for validation (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Two validation studies both used prospective cohorts (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). Four studies conducted population-level analyses (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e) (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eModelling methods\u003c/h2\u003e\n \u003cp\u003eWe included 12 development studies, all developed a single prediction model, 10 using traditional logistic regression (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e), one applying penalised logistic regression with maximum likelihood estimation (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), and one using Bayesian regression (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eType of validation\u003c/h2\u003e\n \u003cp\u003eLu 2020 (\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e) developed a model without validation. The other 11 internally validated their models using either bootstrapping (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), cross-validation (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e), random split (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e) techniques, or an observational cohort from the same institution during the trial period (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Six development studies further externally assessed their models on separate populations who were different from the training cohort: three conducted temporal validation (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e), one used geographical validation (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e), and two performed independent validation (one within the same setting (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e) and another in different setting (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e)) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). One validation-only study validated Levine 2018 (the United States), Migliorelli 2019 (Spain), and Rossi 2020 (the United States) models in the Spanish context (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e). Another validation-only study validated Levine 2018 in the French context (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). Two development studies also validated Levine\u0026rsquo;s 2018 model in the United States (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e) and Rossi\u0026rsquo;s 2020 model in China (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003ePopulations\u003c/h2\u003e\n \u003cp\u003eThe median sample size for training datasets of 12 development studies was 809 and ranged from 200 (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e) to 4,177,644 (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e), and the median rate of CS in the training datasets was 20.7% and ranged from 7.4% (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e) to 40.9% (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Six studies included women with unfavourable cervix status only (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), two studies included nulliparous women only (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e), one study focused on obese women (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e), one study focused on pregnancies with small-for-gestational-age fetus (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e), and one study focused on women without medical indications of induction (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eOutcome of interest\u003c/h2\u003e\n \u003cp\u003eAll eligible studies developed/validated a prediction model for CS following IOL, with one study focused on one type of induction method (Dinoprostone insertion) (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), two studies only included women who underwent cervical ripening (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e), and one study only predicted CS for failure to progress (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eModel accessibility\u003c/h2\u003e\n \u003cp\u003eAll development studies provided a formula for their final models, with four studies further presented as nomograms (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e) and three studies presented as interactive web-based applications (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictors in the final model\u003c/h2\u003e\n \u003cp\u003eThe median number of predictors included in the final model was seven and ranged from three (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e) to 11 (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 38 unique predictors were included in the final models, with 22 appearing only once (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Common predictors included in the final models were maternal height (n\u0026thinsp;=\u0026thinsp;8) (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e), maternal age (n\u0026thinsp;=\u0026thinsp;7) (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e), parity (n\u0026thinsp;=\u0026thinsp;7) (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), and gestational age at birth/induction (n\u0026thinsp;=\u0026thinsp;7) (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e). Three studies included ultrasound assessments as predictors (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). Maternal age was treated as a continuous variable only. Three studies categorised BMI (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e), and four studies categorised maternal height (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e) (Table S3).\u003c/p\u003e\n \u003cp\u003eIn terms of the direction of these predictor factors, several studies reported a higher probability of CS following IOL for shorter maternal height (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e), older maternal age (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e), nulliparity (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e), higher BMI or maternal weight, or obesity measured at different time points (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e), later gestational age (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e), and lower Bishop score (either modified or not) (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e). Interestingly, one study (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e) included the predicted probability of CS using a previously published model as a predictor (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e) in its model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003ePerformance measurement\u003c/h2\u003e\n \u003cp\u003eFive development studies and one validation-only study evaluated their performance using all calibration, discrimination, and classification measurements (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e) (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Only one validation-only study assessed clinical utility using decision-curve analysis (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e), and none evaluated fairness. The Area Under the Receiver Operating Characteristic curve (AUROC) was reported in all eligible studies, ranging from 0.71 (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e) to 0.88 (\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e) for internal validations, and 0.67 (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e) to 0.82 (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e) for external validations in 12 development studies (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Calibration was assessed in 11 studies using the Hosmer\u0026ndash;Lemeshow test (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e) or calibration plots/curves (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e) (Table S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eQuality assessment\u003c/h2\u003e\n \u003cp\u003eOverall, two studies (one development and one validation-only) were classified as having a low risk of bias (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e), and 11 had a low concern of applicability (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Three studies included predictors available after the start of induction (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), which raised a high concern about their applicability to our review question, which is to prognostically predict the risk of CS before the start of induction. The domain of analysis was identified as the main contributor to the high risk of bias in our review (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Eligible studies predominantly selected candidate predictors through univariate analysis prior to conducting multivariable modelling, indicating a potential risk of bias in the analysis process (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e) (Table S5). Events per variable (EPV) of one study was less than 10 (\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e), and of four studies was less than 20 (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e), indicating that there was not a reasonable number of populations with the outcome of interest. The handling of missing data was not reported in five studies (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2: Risk of bias assessment of eligible studies\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cimg 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\"\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of externally validated models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValidated Model\u003c/p\u003e\n \u003cp\u003e(Author Year Country Ref.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStudy ID\u003c/p\u003e\n \u003cp\u003e(Author Year Country)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal population\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCaesarean section, %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUROC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCalibration\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical utility\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk of bias\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDanilack 2020 USA\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDanilack 2020 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,282, 7.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u0026ndash;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDanilack 2020 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154, 7.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eJochum 2019 France\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJochum 2019 France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250, 24.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u0026ndash;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJochum 2019 USA*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e790, 18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026ndash;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevine 2018 USA\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevine 2018 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136, 27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u0026ndash;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevine 2018 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,235, 26.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u0026ndash;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJochum 2019 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e790, 18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026ndash;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL\u0026oacute;pez-Jim\u0026eacute;nez 2022 Spain (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148, 31.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u0026ndash;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTollon 2023 France (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192, 32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u0026ndash;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigliorelli 2019 Spain\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMigliorelli 2019 Spain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70, 20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026ndash;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL\u0026oacute;pez-Jim\u0026eacute;nez 2022 Spain (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99, 30.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u0026ndash;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRossi 2019 USA\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRossi 2019 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,098,981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273,184, 24.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u0026ndash;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRossi 2019 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197,982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48,881, 24.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u0026ndash;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eRossi 2020 USA\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRossi 2020 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,177,644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800,423, 19.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026ndash;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRossi 2020 USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e940,383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u0026ndash;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL\u0026oacute;pez-Jim\u0026eacute;nez 2022 Spain (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148, 31.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u0026ndash;0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhou 2022 China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317, 16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eZhou 2022 China\u003c/strong\u003e (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhou 2022 China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e392, 13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u0026ndash;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhou 2022 China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317, 16.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u0026ndash;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e☑\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValidation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eAbbreviations: N/R\u0026thinsp;=\u0026thinsp;Not Reported; AUROC\u0026thinsp;=\u0026thinsp;Area Under the Receiver Operating Characteristic curve; CI\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003e*The same study used a USA cohort for validation. ☑ = Reported.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eMain Findings\u003c/h2\u003e\u003cp\u003eIn total, 14 studies (12 development and two validation-only) published between 2017 and 2023 were included in our systematic review. All development studies used statistical methods (logistic or Bayesian regression), 11 models were internally validated, and seven were also externally validated. All studies reported discrimination performance using AUROC, 11 reported calibration performance, only one validation-only study assessed clinical utility using decision-curve analysis, and none evaluated fairness. Overall, 12 studies had unclear or high risk of bias, mainly attributable to the selection of predictors through univariate analysis. Three studies raised applicability concerns due to the inclusion of predictors not available before induction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003e This is the first review to use the TRIPOD\u0026thinsp;+\u0026thinsp;AI and PROBAST criteria to assess the risk of bias, model applicability, and reporting quality of prediction models for term CS following IOL. The identification of low reporting quality, high risk of bias, and high concern of model applicability in the existing models can guide researchers in overcoming these limitations and developing more robust, reliable, and generalised prediction models.\u003c/p\u003e\u003cp\u003eWe originally aimed to conduct a meta-analysis comparing model performance between statistical and machine learning approaches. However, as all included studies employed statistical methods, such an analysis was not feasible.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eComparison with existing literature\u003c/h2\u003e\u003cp\u003eOur findings echo previous systematic reviews of prediction models for maternal outcomes, revealing widespread methodological weaknesses and poor reporting (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Particularly, the high risk of bias due to the use of univariate analysis for candidate predictor selection and the presence of a low EPV ratio.\u003c/p\u003e\u003cp\u003eCommon predictors identified in our review for higher CS following IOL were shorter maternal height, higher maternal weight, older maternal age, nulliparity, low Bishop score, and later gestational age at birth/induction, which aligned with previous studies identifying risk factors associated with CS following IOL (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). It is important to acknowledge that the cervical status of women can change rapidly in clinical practice (time-varying variable). Therefore, future research should clearly specify the timing of measurement to account for these dynamic changes and ensure the accuracy and applicability of predictive models in clinical practice.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eClinical implications\u003c/h2\u003e\u003cp\u003eClinical utility refers to the consequences of decisions made based on the output of a prediction model and is distinct from discrimination and calibration measurements. It is commonly assessed using decision curve analysis, which estimates the net benefit of applying the model across a range of threshold probabilities (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Net benefit quantifies the trade-off between the benefit of correctly predicting true positives and the harm of incorrectly predicting false positives. A clinically reasonable range of threshold probabilities can be determined by considering the maximum number of false positives that health care providers and systems are generally willing to tolerate in order to correctly identify one true positive, and it critically depends on the clinical context. In most cases, this threshold does not exceed 50% as missing a true positive is generally considered more harmful than a false positive case (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our review, only one validation-only study examined the clinical utility of two included models (Levin 2018 and Rossi 2020) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, their interpretation of the results was inappropriate as they reversed the proper sequence between threshold probability and model evaluation. Specifically, the decision curve results should not be used to set the range of threshold probability for the model. Instead, a clinically reasonable range of threshold probabilities should be defined first, and then used to evaluate which model provides the highest net benefit across that range (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). To date, no such threshold range has been determined for IOL, warranting future qualitative research.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eAlthough the included prediction models for term CS following IOL exhibited acceptable or excellent discrimination, most are subject to methodological limitations (e.g. univariate analysis for candidate predictor selection or inclusion of predictors known after or during induction). Clinical utility was rarely assessed, and none of the studies evaluated model fairness. Future research should prioritise the inclusion of comprehensive performance measures and strengthen methodological rigour and reporting transparency to improve model reliability and clinical applicability, as emphasised in existing reporting guidelines.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003cp\u003eArea Under the Receiver Operating Characteristic curve (AUROC)\u003c/p\u003e\n\u003cp\u003eBody Mass Index (BMI)\u003c/p\u003e\n\u003cp\u003eCaesarean Section (CS)\u003c/p\u003e\n\u003cp\u003eConfidence Interval (CI)\u003c/p\u003e\n\u003cp\u003eInduction Of Labour (IOL)\u003c/p\u003e\n\u003cp\u003eProspective Register Of Systematic Reviews (PROSPERO)\u003c/p\u003e\n\u003cp\u003ePrediction model Risk Of Bias ASsessment Tool (PROBAST)\u003c/p\u003e\n\u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)\u003c/p\u003e\n\u003cp\u003eCHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)\u003c/p\u003e\n\u003cp\u003eTransparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis\u0026nbsp;+ Artificial Intelligence\u0026nbsp;(TRIPOD+AI)\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable.\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:\u0026nbsp;\u003c/strong\u003eAlldata generated or analysed during this study are included in this published article and its supplementary files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e No competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Emily Callander receives salary support from the National Health and Medical Research Council (NHMRC) through fellowship schemes. Yanan HU receives support from the Australian Government Research Training Program (RTP) Scholarship. The funders were not involved in the preparation of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u003c/strong\u003e Yanan Hu led the study conception and design, development of the search strategy, database searching, screening and selection of studies, data extraction and synthesis, interpretation of the findings, and drafting of the manuscript. Xin Zhang screened titles and abstracts, reviewed full-text articles, and checked the results of data extraction and quality appraisal. Swapna Gokhale screened titles and abstracts. Valerie Slavin, Joanne Enticott and Emily Callander contributed to the study conception, interpretation of the findings, and editing of the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCoates D, Makris A, Catling C, Henry A, Scarf V, Watts N, et al. A systematic scoping review of clinical indications for induction of labour. PLoS One. 2020;15(1):e0228196.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO recommendations: induction of labour at or beyond term: World Health Organization; 2018.\u003c/li\u003e\n\u003cli\u003eTarimo CS, Mahande MJ, Obure J. Prevalence and risk factors for caesarean delivery following labor induction at a tertiary hospital in North Tanzania: a retrospective cohort study (2000\u0026ndash;2015). BMC Pregnancy and Childbirth. 2020;20(1):173.\u003c/li\u003e\n\u003cli\u003eJeong Y, Choo SP, Yun J, Kim EH. Effect of maternal age on maternal and perinatal outcomes including cesarean delivery following induction of labor in uncomplicated elderly primigravidae. Medicine. 2021;100(34).\u003c/li\u003e\n\u003cli\u003eBjorklund J, Wiberg-Itzel E, Wallstrom T. Is there an increased risk of cesarean section in obese women after induction of labor? A retrospective cohort study. PLOS ONE. 2022;17(2):e0263685.\u003c/li\u003e\n\u003cli\u003eObeidat RA, Almaaitah M, Ben-Sadon A, Istaiti D, Rawashdeh H, Hamadneh S, et al. Clinical predictive factors for vaginal delivery following induction of labour among pregnant women in Jordan. BMC Pregnancy and Childbirth. 2021;21(1):685.\u003c/li\u003e\n\u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.\u003c/li\u003e\n\u003cli\u003eCollins GS, Moons KGM, Debray TPA, Altman DG, Riley RD. Systematic Reviews of Prediction Models. Systematic Reviews in Health Research2022. p. 347-76.\u003c/li\u003e\n\u003cli\u003eMoons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS medicine. 2014;11(10):e1001744.\u003c/li\u003e\n\u003cli\u003eCollins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.\u003c/li\u003e\n\u003cli\u003eWolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Annals of internal medicine. 2019;170(1):51-8.\u003c/li\u003e\n\u003cli\u003eDanilack VA, Hutcheon JA, Triche EW, Dore DD, Muri JH, Phipps MG, et al. Development and Validation of a Risk Prediction Model for Cesarean Delivery after Labor Induction. Journal of Women\u0026apos;s Health. 2020;29(5):656-69.\u003c/li\u003e\n\u003cli\u003eLevine LD, Downes KL, Parry S, Elovitz MA, Sammel MD, Srinivas SK. A validated calculator to estimate risk of cesarean after an induction of labor with an unfavorable cervix. American Journal of Obstetrics \u0026amp; Gynecology. 2018;218(2):254.e1-.e7.\u003c/li\u003e\n\u003cli\u003eNwabuobi C, Gowda N, Schmitz J, Wood N, Pargas A, Bagiardi L, et al. Risk factors for Cesarean delivery in pregnancy with small-for-gestational-age fetus undergoing induction of labor. Ultrasound in Obstetrics \u0026amp; Gynecology. 2020;55(6):799-805.\u003c/li\u003e\n\u003cli\u003eRossi RM, Requarth E, Warshak CR, Dufendach KR, Hall ES, DeFranco EA. Risk Calculator to Predict Cesarean Delivery Among Women Undergoing Induction of Labor. Obstetrics \u0026amp; Gynecology. 2020;135(3):559-68.\u003c/li\u003e\n\u003cli\u003eRossi RM, Requarth EW, Warshak CR, Dufendach K, Hall ES, Defranco EA. Predictive Model for Failed Induction of Labor among Obese Women. Obstetrics \u0026amp; Gynecology. 2019;134(3):485-93.\u003c/li\u003e\n\u003cli\u003eMigliorelli F, Ba\u0026ntilde;os N, Angeles MA, Rueda C, Salazar L, Gratac\u0026oacute;s E, et al. Clinical and sonographic model to predict cesarean delivery after induction of labor at term. Fetal Diagnosis and Therapy. 2019;46(2):88-96.\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez-Jim\u0026eacute;nez N, Garc\u0026iacute;a-S\u0026aacute;nchez F, Hern\u0026aacute;ndez-Pailos R, Rodrigo-\u0026Aacute;lvaro V, Pascual-Pedre\u0026ntilde;o A, Moreno-Cid M, et al. Risk of caesarean delivery in labour induction: a systematic review and external validation of predictive models. BJOG. 2022;129(5):685-95.\u003c/li\u003e\n\u003cli\u003eJochum F, Le Ray C, Blanc-Petitjean P, Langer B, Meyer N, Severac F, et al. Externally Validated Score to Predict Cesarean Delivery After Labor Induction With Cervi Ripening. Obstetrics \u0026amp; Gynecology. 2019;134(3):502-10.\u003c/li\u003e\n\u003cli\u003eTollon P, Blanc-Petitjean P, Drumez E, Ghesqui\u0026egrave;re L, Le Ray C, Garabedian C. Prediction of successful labor induction with very unfavorable cervix: A comparison of six scores. International Journal of Gynecology \u0026amp; Obstetrics. 2023;160(1):53-8.\u003c/li\u003e\n\u003cli\u003eZhou H, Gu N, Yang Y, Wang Z, Hu Y, Dai Y. Nomogram predicting cesarean delivery undergoing induction of labor among high-risk nulliparous women at term: a retrospective study. BMC Pregnancy and Childbirth. 2022;22(1):55.\u003c/li\u003e\n\u003cli\u003eLu J, Cheng YKY, Ho SYS, Sahota DS, Hui LL, Poon LC, et al. The predictive value of cervical shear wave elastography in the outcome of labor induction. Acta Obstet Gynecol Scand. 2020;99(1):59-68.\u003c/li\u003e\n\u003cli\u003eHemmatzadeh S, Abbasalizadeh F, Mohammad-Alizadeh-Charandabi S, Asghari Jafarabadi M, Mirghafourvand M. Development and Validation of a Nomogram to Estimate Risk of Cesarean After Induction of Labor in Term Pregnancies with an Unfavorable Cervix in Iran. Clinical nursing research. 2022;31(7):1332-9.\u003c/li\u003e\n\u003cli\u003eBademkiran MH, Bademkiran C, Ege S, Peker N, Sucu S, Obut M, et al. Explanatory variables and nomogram of a clinical prediction model to estimate the risk of caesarean section after term induction. Journal of Obstetrics and Gynaecology. 2021;41(3):367-73.\u003c/li\u003e\n\u003cli\u003eKamel RA, Negm SM, Youssef A, Bianchini L, Brunelli E, Pilu G, et al. Predicting cesarean delivery for failure to progress as an outcome of labor induction in term singleton pregnancy. American Journal of Obstetrics \u0026amp; Gynecology. 2021;224(6):609.e1-.e11.\u003c/li\u003e\n\u003cli\u003eMeier K, Parrish J, D\u0026apos;Souza R. Prediction models for determining the success of labor induction: A systematic review. Acta Obstet Gynecol Scand. 2019;98(9):1100-12.\u003c/li\u003e\n\u003cli\u003eTolcher MC, Holbert MR, Weaver AL, McGree ME, Olson JE, El-Nashar SA, et al. Predicting Cesarean Delivery After Induction of Labor Among Nulliparous Women at Term. Obstetrics \u0026amp; Gynecology. 2015;126(5):1059-68.\u003c/li\u003e\n\u003cli\u003eQuach D, ten Eikelder M, Jozwiak M, Davies-Tuck M, Bloemenkamp KWM, Mol BW, et al. Maternal and fetal characteristics for predicting risk of Cesarean section following induction of labor: pooled analysis of PROBAAT trials. Ultrasound in Obstetrics \u0026amp; Gynecology. 2022;59(1):83-92.\u003c/li\u003e\n\u003cli\u003eVickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagnostic and Prognostic Research. 2019;3(1):18.\u003c/li\u003e\n\u003cli\u003eWynants L, van Smeden M, McLernon DJ, Timmerman D, Steyerberg EW, Van Calster B. Three myths about risk thresholds for prediction models. BMC Med. 2019;17(1):192.\u003c/li\u003e\n\u003c/ol\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-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"birth, caesarean section, clinical prediction model, induction of labour, TRIPOD + AI, reporting quality","lastPublishedDoi":"10.21203/rs.3.rs-7331090/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7331090/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Model development and validation constitute the critical initial steps in the clinical prediction modelling pipeline, where methodological rigour and reporting transparency determine the validity of all subsequent stages. Our systematic review aimed to appraise the methodology and reporting quality of recently published studies that developed or externally validated prediction models for caesarean section at term following induction of labour.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: MEDLINE, Scopus, Embase, IEEE Xplore and CINAHL Complete databases were searched to identify original research studies published since 2017. Studies that did not report any performance measurement of the prediction model were excluded. No restrictions were applied to study designs, populations, modelling algorithms, type and timing of predictors, and methods of induction. Descriptive analysis was performed to assess heterogeneity across eligible studies. Study risk of bias and model applicability were assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Study reporting quality was evaluated using the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD+AI) Statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Among the 14 included studies, 12 developed a single model using logistic or Bayesian regression, and two externally validated included models. All studies reported discrimination performance using the area under the receiver operating characteristic (AUROC) curve, and 11 reported calibration performance. However, only one validation-only study assessed clinical utility using decision-curve analysis, and none evaluated model fairness. Overall, 12 studies had unclear or high risk of bias, mainly attributable to the selection of predictors through univariate analysis. Three studies raised applicability concerns due to the inclusion of predictors not available before induction. The handling of missing data was not reported in five studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: More external validation is needed to assess the clinical utility and fairness of existing models before they can be recommended for the subsequent stages in the clinical prediction pipeline and ultimately support clinical decision-making and improve maternal and neonatal outcomes. Future research should prioritise the inclusion of comprehensive performance measures and strengthen methodological rigour and reporting transparency to improve model reliability and clinical applicability, as emphasised in existing reporting guidelines.\u003c/p\u003e","manuscriptTitle":"Prediction models for caesarean section following induction of labour: A systematic review of methodology and reporting quality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 19:10:37","doi":"10.21203/rs.3.rs-7331090/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T10:00:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T13:32:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T06:04:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22259231734177794327726361080286457428","date":"2025-09-24T11:12:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-16T11:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107678815846317802240924059393465352002","date":"2025-09-16T11:45:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87305946107159898859759745025384660789","date":"2025-09-08T06:02:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T19:31:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-13T18:47:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T16:26:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T16:24:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2025-08-09T03:44:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65633e7a-a383-497b-82ea-022927129c11","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:06:50+00:00","versionOfRecord":{"articleIdentity":"rs-7331090","link":"https://doi.org/10.1186/s12874-026-02767-7","journal":{"identity":"bmc-medical-research-methodology","isVorOnly":false,"title":"BMC Medical Research Methodology"},"publishedOn":"2026-01-27 15:59:11","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-09-11 19:10:37","video":"","vorDoi":"10.1186/s12874-026-02767-7","vorDoiUrl":"https://doi.org/10.1186/s12874-026-02767-7","workflowStages":[]},"version":"v1","identity":"rs-7331090","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7331090","identity":"rs-7331090","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}