Patient-Led Approaches to a Vaginal Birth After Cesarean Delivery Calculator
Bibliographic record
Abstract
OBJECTIVE: To describe patient approaches to navigating their probability of a vaginal birth after cesarean (VBAC) within the context of prediction scores generated from the original Maternal-Fetal Medicine Units' VBAC calculator, which incorporated race and ethnicity as one of six risk factors. METHODS: We invited a diverse group of participants with a history of prior cesarean delivery to participate in interviews and have their prenatal visits recorded. Using an open-ended iterative interview guide, we queried and observed these individuals' mode-of-birth decisions in the context of their VBAC calculator scores. We used a critical and feminist approach to analyze thematic data gleaned from interview and visit transcripts. RESULTS: Among the 31 participants who enrolled, their self-identified racial and ethnic categories included: Asian or South Asian (2); Black (4); Hispanic (12); Indigenous (1); White (8); and mixed-Black, -Hispanic, or -Asian background (4). Predicted VBAC success probabilities ranged from 12% to 95%. Participants completed 64 interviews, and 14 prenatal visits were recorded. We identified four themes that demonstrated a range of patient-led approaches to interpreting the probability generated by the VBAC calculator: 1) rejecting the role of race and ethnicity; 2) reframing failure, finding success; 3) factoring the physical experience of labor; and 4) modifying the probability for VBAC. CONCLUSION: Our findings demonstrate that a numeric probability for VBAC may not be highly valued or important to all patients, especially those who have strong intentions for VBAC. Black and Hispanic participants challenged the VBAC calculator's incorporation of race and ethnicity as a risk factor and resisted the implication it produced, especially that their bodies were less capable of achieving a vaginal birth. Our findings suggest that patient-led approaches to assessing and interpreting VBAC probability may be an untapped resource for achieving a more person-centered, equitable approach to counseling.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".