Effect of Uterine Rupture on a Hospital's Future Rate of Vaginal Birth After Cesarean Delivery
Bibliographic record
Abstract
OBJECTIVE: To identify whether a hospital's vaginal birth after cesarean delivery rate, trial of labor after cesarean delivery rate, or trial of labor success rate decrease after the occurrence of a uterine rupture. METHODS: The study population was drawn from the Nationwide Inpatient Sample, a sample of U.S. hospitals, between 1998 and 2010. We extracted deliveries to women with a previous cesarean delivery. International Classification of Diseases, 9th Revision, Clinical Modification codes were used to identify severe uterine ruptures and rates of vaginal birth, trial of labor, and trial of labor success. We used the difference-in-differences design and compared the rates of the outcomes before and after a rupture across hospitals using hospitals without ruptures to control for secular trends. Included in the analysis were 1,202,284 delivery records from 7,975 hospital-years without ruptures and 211,850 records from 510 hospital-years with uterine ruptures. RESULTS: Before the occurrence of a severe uterine rupture, there were an estimated 60 successful vaginal deliveries for every 100 women with a previous cesarean delivery who entered labor. In the month after the rupture, the trial of labor success rate decreased by an estimated 25 cases per 1,000 labors (95% confidence interval [CI] 6-44/1,000, P=.01) before returning to baseline. The percentage of women with a previous cesarean delivery who attempted vaginal delivery did not significantly change after the rupture. Overall, there were 17 more cesarean deliveries per 1,000 women with a previous cesarean delivery (95% CI 4-31/1,000, P=.01) in the month after the uterine rupture. CONCLUSION: The decrease in the trial of labor success rate after a recent uterine rupture is likely the result of short-term changes in risk evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".