Impact of prior use of topical hemostatic agents on trial of labor after cesarean: Insights from a multicenter cohort study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between a topical hemostatic agent used at the time of cesarean delivery and uterine scar disruption (rupture or dehiscence) at the subsequent trial of labor after cesarean (TOLAC). METHODS: A multicenter retrospective cohort study was conducted (2005-2021). Parturients with a singleton pregnancy in whom a topical hemostatic agent was placed during the primary cesarean delivery were compared with patients in whom no such agent was placed. We assessed the uterine scar disruption rate after the subsequent TOLAC and the rate of adverse maternal outcomes. Univariate analyses were followed by multivariate analysis (adjusted odds ratio [aOR]; 95% confidence interval [CI]). RESULTS: During the study period, 7199 women underwent a trial of labor and were eligible for the study; 430 (6.0%) had prior use of a hemostatic agent, 6769 (94.0%) did not. In univariate analysis, a history of topical hemostatic agent use was not found to be significantly associated with uterine scar rupture, dehiscence, or failed trial of labor. This was also confirmed on multivariate analysis for uterine rupture (aOR 1.91, 95% CI 0.66-5.54; P = 0.23), dehiscence of uterine scar (aOR 1.62, 95% CI 0.56-4.68; P = 0.37), and TOLAC failure (aOR 1.08, 95% CI 0.79-1.48; P = 0.61). CONCLUSION: A history of hemostatic agent use is not associated with an increased risk for uterine scar disruption after subsequent TOLAC. Further prospective studies in other settings are needed to strengthen these findings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".