Should we allow a trial of labor after a previous cesarean for dystocia in the second stage of labor?
Bibliographic record
Abstract
OBJECTIVE: To estimate the rate of successful vaginal birth including operative vaginal delivery in patients with a previous cesarean for cephalopelvic disproportion in the second stage of labor. METHODS: Data from all patients who underwent trial of labor after a previous cesarean between 1990 and 2000 at our tertiary care institution were analyzed. Medical records were reviewed and data collected for the following variables: indication for the previous cesarean, birth weight and cervical dilatation at previous cesarean delivery, as well as the mode of delivery (spontaneous, vacuum, forceps, cesarean) and the birth weight for the subsequent pregnancy. Pearson's chi(2) test and one-way analysis of variance were used for statistical analyses. RESULTS: There were 2002 patients included in the study. Two hundred fourteen (11%) had their previous cesarean for dystocia in the second stage of labor, 654 (33%) for dystocia in the first stage of labor, and 1134 (57%) for other indications. The vaginal birth after cesarean success rate was 75.2% (P = .015 vs other indications), 65.6% (P < .001 vs other indications), and 82.5%, respectively. The rate of operative vaginal delivery was 15%, 12%, and 10% (P = .109). CONCLUSION: A trial of labor is reasonable in women whose previous cesarean was for dystocia in the second stage of labor. In this series, patients who underwent a trial of labor after a previous cesarean for dystocia in the second stage had 75.2% (95% confidence interval 69.5, 81.0) chance of achieving vaginal delivery.
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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 | 0.003 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".