Mode of Delivery: Toward Responsible Inclusion of Patient Preferences
Bibliographic record
Abstract
Deciding when and how to incorporate patient preferences regarding mode of delivery is challenging for both obstetric providers and policymakers. An analysis of current guidelines in four clinical scenarios (prior cesarean, twin delivery, breech presentation, and maternal request for cesarean) indicates that some guidelines are highly prescriptive whereas others are more flexible, based on physicians' discretion or (less frequently) patient preferences, without consistency or explicit rationale for when such flexibility is permissible, advisable, or obligatory. Although patient-choice advocates have called for more patient-responsive guidelines, concerns also have been raised, especially in the context of discussions of cesarean delivery on maternal request, about the dangers of unfettered patient-preference-driven clinical decisions. In this article, we outline a framework for the responsible inclusion of patient preferences into decision making regarding approach to delivery. We conclude, using this framework, that more explicit incorporation of patient preferences is called for in the first three scenarios and indicate why expanding access to cesarean delivery on maternal request is more complicated and would require more data and further consideration.
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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 | 0.200 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| 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".