Predicting Success and Reducing the Risks When Attempting Vaginal Birth After Cesarean
Bibliographic record
Abstract
The goal of this manuscript is to review the contemporary evidence on issues pertinent to improving the safety profile of vaginal birth after cesarean (VBAC) attempts. Patients attempting VBAC have success rates of 60%–80%, and no reliable method of predicting VBAC failure for individual patients exists. The rate of uterine rupture in all patients ranges from 0.7% to 0.98%, but the rate of uterine rupture decreases in patients with a prior vaginal delivery. In fact, in patients with a prior vaginal delivery, VBAC appears to be safer from the maternal standpoint than repeat cesarean. Inevitably, the obstetrician today will encounter the situation of deciding whether or not to induce a patient with a uterine scar, and particular attention is paid to the success and risks of inducing labor in this patient population. Induction of labor is associated with a slightly lower successful vaginal delivery rate, although the rate remains above 50% in virtually all patient populations. The rate of uterine rupture increases slightly, but still remains around 2%–3%. Although misoprostol use is discouraged due to its association with increased risks of uterine rupture, transcervical catheters, oxytocin, and amniotomy may be used to induce labor in women attempting VBAC. Target Audience: Obstetricians & Gynecologists, Family Physicians Learning Objectives: After completion of this article, the reader should be able to summarize recent literature regarding vaginal birth after cesarean and list factors related to labor induction success among women with a history of cesarean delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".