The Development of a Prediction Model for Vaginal Birth After Cesarean Section (VBAC) [34P]
Bibliographic record
Abstract
INTRODUCTION: Pregnant women who have had a previous cesarean section face the decision to either undergo a repeat elective cesarean or to attempt vaginal birth after cesarean (VBAC). The goal of this project was to determine the factors that predict success of VBAC in women with one prior cesarean section in the population of British Columbia, Canada. METHODS: Data were drawn from the British Columbia Perinatal Data Registry. We performed a retrospective analysis of 8291 cephalic term singleton planned vaginal births in British Columbia to women with a previous cesarean. Using block multivariate logistic regression, we analyzed the variables, both modifiable and non-modifiable, most predictive of successful VBAC. A result was considered statistically significant if its p value was <.05. RESULTS: Of the 8291 planned VBAC births, 5960 were successful, while 2331 resulted in cesarean deliveries. Predictive variables were: maternal age, height and BMI, whether or not she had a vaginal birth within 2 years of the planned VBAC, whether she had a recurrent indication for the prior cesarean, whether she was administered oxytocin or prostaglandins for labor augmentation or induction, whether she was given an epidural, and the size of hospital in which she gave birth. CONCLUSION: A prediction model, incorporating eight variables has been developed. This model can inform shared decision-making and guide individualized counselling for eligible women on the chance of VBAC success. We hope that this prediction model will contribute to the literature and ultimately lower the probability of complications and adverse outcomes in childbirth.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".