Effect of Prior Vaginal Delivery or Prior Vaginal Birth After Cesarean Delivery on Obstetric Outcomes in Women Undergoing Trial of Labor
Bibliographic record
Abstract
OBJECTIVE: We sought to study the effects of prior vaginal delivery or prior vaginal birth after cesarean delivery (VBAC) on the success of a trial of labor after a cesarean delivery. METHODS: An observational study of patients who underwent a trial of labor after a single low-transverse cesarean delivery. Patients with a previous cesarean delivery and no vaginal birth were compared with patients with a single vaginal delivery before or after the previous cesarean delivery. The rates of successful VBAC, uterine rupture, and scar dehiscence were analyzed. Multivariable regression was performed to adjust for confounding variables. RESULTS: Of 2,204 patients, 1,685 (76.4%) had a previous cesarean delivery and no vaginal delivery, 198 (9.0%) had a vaginal delivery before the cesarean delivery, and 321 (14.6%) had a prior VBAC. The rate of successful trial of labor was 70.1%, 81.8%, and 93.1%, respectively (P <.001). A prior VBAC was associated with fewer third- and fourth-degree lacerations (8.5% versus 2.5% versus 3.7%, P <.001) and fewer operative vaginal deliveries (14.7% versus 5.6% versus 1.9%, P <.001) but not with uterine rupture (1.5% versus 0.5% versus 0.3%, P =.12). Patients with a prior VBAC had, in addition, a higher rate of uterine scar dehiscence (21.8%) compared with patients with a previous cesarean delivery and no vaginal delivery (5.3%; P =.001). CONCLUSION: A prior vaginal delivery and, particularly, a prior VBAC are associated with a higher rate of successful trial of labor compared with patients with no prior vaginal delivery. In addition, prior VBAC is associated with an increased rate of uterine scar dehiscence.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".