Maternal and neonatal morbidity: repeat Cesarean versus a trial of labour after previous Cesarean delivery
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to perform a meta-analysis comparing the rates of uterine rupture, and other maternal and neonatal complications, between women who undergo a trial of labour (TOL) after a prior Cesarean delivery and those to undergo elective repeat Cesarean delivery (ERCD). SOURCE: Medline, Cochrane, EMBASE and Google Scholar were searched until May 6, 2015 using the keywords/phrases: trial of labour, Cesarean section, elective, repeat, pregnancy and vaginal birth. Randomized controlled trials (RCTs), two-arm prospective studies, one-arm studies and retrospective studies were included. The primary outcome was uterine rupture. PRINCIPAL FINDINGS: Sixteen studies were included in the meta-analysis. TOL after prior Cesarean delivery was associated with higher odds of uterine rupture as compared with ERCD (Peto odds ratio [OR] = 4.685, 95% confidence interval [CI]: 3.077 to 7.133, p < 0.001). TOL was associated with a higher rate of endometritis, a lower rate of hysterectomy, and a lower rate of respiratory problems in newborns. There were no differences between the groups with respect to neonatal intensive care unit admissions, postpartum hemorrhage, thromboembolic disease, sepsis and neonatal mortality. CONCLUSIONS: TOL may be associated with a higher risk of uterine rupture and endometritis, but lower risk of hysterectomy and neonatal respiratory problems than ERCD.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".