Derivation and validation of a model predicting the likelihood of vaginal birth following labour induction
Bibliographic record
Abstract
BACKGROUND: There is high-quality evidence supporting induction of labour (IOL) for a number of maternal and fetal indications. However, one fifth of inductions fail to result in vaginal births, requiring cesarean deliveries. This has negative clinical, emotional and resource implications. The importance of predicting the success of labour induction to enable shared decision-making has been recognized, but existing models are limited in scope and generalizability. Our objective was to derive and internally validate a clinical prediction model that uses variables readily accessible through maternal demographic data, antenatal history, and cervical examination to predict the likelihood of vaginal birth following IOL. METHODS: Data was extracted from electronic medical records of consecutive pregnant women who were induced between April and December 2016, at Mount Sinai Hospital, Toronto, Canada. A multivariable logistic regression model was developed using 16 readily accessible variables identified through literature review and expert opinion, as predictors of vaginal birth after IOL. The final model was internally validated using 10-fold cross-validation. RESULTS: Of the 1123 cases of IOL, 290 (25.8%) resulted in a cesarean delivery. The multivariable logistic regression model found maternal age, parity, pre-pregnancy body mass index and weight, weight at delivery, and cervical dilation at time of induction as significant predictors of vaginal delivery following IOL. The prediction model was well calibrated (Hosmer-Lemeshow χ2 = 5.02, p = 0.76) and demonstrated good discriminatory ability (area under the receiver operating characteristic (AUROC) curve, 0.81 (95% CI 0.78 to 0.83)). Finally, the model showed good internal validity [AUROC 0.77 (95% CI 0.73 to 0.82)]. CONCLUSIONS: We have derived and internally validated a well-performing clinical prediction model for IOL in a large and diverse population using variables readily accessible through maternal demographic data, antenatal history, and cervical examination. Once prospectively validated in diverse settings, and if shown to be acceptable to pregnant women and healthcare providers as well as clinically and cost-effective, this model has potential for widespread use in clinical practice and research for enhancing patient autonomy, improving induction outcomes, and optimizing allocation of resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".