The influence of patient preferences and physician practices on cesarean delivery.
Bibliographic record
Abstract
UNLABELLED: PURPOSE OF INVESTIGATIONS: A wide range of variation exists between the cesarean delivery rates of individual obstetricians in our health district, despite an overall cesarean delivery rate below the national average. This study tested the hypothesis that physician and patient determinants influenced the decision to perform a cesarean delivery by investigating its preventability at a tertiary care medical center. MATERIALS AND METHODS: A retrospective analysis of the medical records of 290 unselected patients who had a term primary cesarean delivery during a twelve-month period was conducted. Patient characteristics, indications for, and preventability of cesarean delivery were determined for each patient. RESULTS: Overall, 66 (23%) of the 290 term cesarean deliveries were deemed preventable: 41 (62%) of the 66 cases were deemed preventable by patients and the other 25 (38%) by obstetricians. The preventable cesarean section rate was significantly higher for local residents as compared to referrals (27% vs. 15%; p = 0.001), the two main indications accounting for preventability being dystocia (53%) and breech presentation (23%). CONCLUSION: The preferences of patients and practices of obstetricians influence recourse to cesarean delivery. Addressing the practices for the clinical management of breech and dystocia by obstetricians and the preferences of patients for their choice of mode of delivery will facilitate the appropriate utilization of cesarean delivery.
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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 | 0.018 |
| 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) | 0.002 | 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".