Vaginal Uncomplicated Delivery Rate as a Quality Indicator Compared to Cesarean Delivery Rate: A Quantitative Analysis of a Population Database
Bibliographic record
Abstract
OBJECTIVES: The objective of this study is to compare the vaginal uncomplicated delivery (VUD) rate, defined as all vaginal deliveries (including forceps and vacuum) without an adverse maternal or neonatal labour outcome, to the cesarean delivery (CD) rate, as a performance indicator. METHODS: This is a retrospective cohort analysis from a provincial database of all term deliveries by an obstetrician in a single year, excluding diagnoses preventing active labour. Most obstetricians in this jurisdiction practice consultative obstetrics, focused on supporting primary maternity care. We investigated the association of adverse delivery (AD), measured by the adverse outcome index, with CD and VUD rates. RESULTS: We report 16 620 deliveries by 210 obstetricians, with a vaginal delivery rate of 39.6%, of which 36.6% were operative vaginal delivery. The overall AD rate was 9.9%, and the overall VUD rate was 34%. While the CD and VUD both correlated with the mode of delivery, only the VUD rate was correlated to the AD rate. CONCLUSIONS: Quality assurance in obstetrics must balance the needs of 2 patients based on limited data. Our data shows the shortcomings of the prevailing performance indicator, CD rate, which does not correlate with birth outcomes for the pregnant patient or infant. The VUD rate provides an alternative that assesses both mode of delivery and labour outcomes. Shifting the quality lens to focus on the VUD rate will provide a better metric that measures optimal outcomes for pregnant people and their babies.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".