Racial and Ethnic Differences in Trial of Labor After Cesarean Attempts and Vaginal Birth After Cesarean Success Rates: A Retrospective Single-Health System Study
Bibliographic record
Abstract
Background: Despite risks associated with repeat cesareans, only 14% of deliveries in the United States were vaginal births after cesareans (VBACs) in 2023. Current research demonstrates that women from minority groups are less likely to be offered a trial of labor after cesarean (TOLAC). The purpose of this study was to determine whether removal of race/ethnicity distinctions from the Maternal-Fetal Medicine Units (MFMU) Network’s VBAC calculator in 2021 resulted in a more equitable distribution of TOLAC and higher VBAC rates. Methods: Retrospective data of patients with a previous cesarean delivery were gathered from January 2017 to June 2024, which was then analyzed utilizing Chi-squared tests and logistic regression models. Results: Overall, 1,905 births were included, of which, 25.62% of patients identified as Hispanic and 12.65% identified as Black. Successful VBAC occurred in 516 (27.09%) births, and 1,389 deliveries were repeat cesareans (72.91%). Importantly, TOLAC rates increased after MFMU’s calculator changes. However, Black patients still had lower odds of VBAC compared to White patients after the calculator change. When evaluating reasons listed as to why patients had a failed TOLAC, it was more often attributed to maternal status for Black patients than White patients. Conclusions: Although changes in MFMU’s VBAC calculator are correlated with increased attempted TOLAC in both Black and Hispanic patients, racial disparities in rates of successful VBAC persist.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".