The Role of Uterine Closure in the Risk of Uterine Rupture
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of prior single-layer compared with double-layer closure on the risk of uterine rupture. METHODS: A multicenter, case-control study was performed on women with a single, prior, low-transverse cesarean who experienced complete uterine rupture during a trial of labor. For each case, three women who underwent a trial of labor without uterine rupture after a prior low-transverse cesarean delivery were selected as control participants. Risk factors such as prior uterine closure, suture material, diabetes, prior vaginal delivery, labor induction, cervical ripening, birth weight, prostaglandin use, maternal age, gestational age, and interdelivery interval were compared between groups. Conditional logistic regression analyses were conducted. RESULTS: Ninety-six cases of uterine rupture, including 28 with adverse neonatal outcome, and 288 control participants were assessed. The rate of single-layer closure was 36% (35 of 96) in the case group and 20% (58 of 288) in the control group (P<.01). In multivariable analysis, single-layer closure (odds ratio [OR] 2.69; 95% confidence interval [CI] 1.37-5.28) and birth weight greater than 3,500 g (OR 2.03; 95% CI 1.21-3.38) were linked with increased rates of uterine rupture, whereas prior vaginal birth was a protective factor (OR 0.47; 95% CI 0.24-0.93). Single-layer closure was also related to uterine rupture associated with adverse neonatal outcome (OR 2.89; 95% CI 1.01-8.27). CONCLUSION: Prior single-layer closure carries more than twice the risk of uterine rupture compared with double-layer closure. Single-layer closure should be avoided in women who could contemplate future vaginal birth after cesarean delivery. LEVEL OF EVIDENCE: II.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".