Impact of Suspending Elective Inductions of Labor on Maternal and Fetal Outcomes
Bibliographic record
Abstract
Background: In July 2021, an academic institution suspended elective inductions of labor (eIOL) after 39 weeks. Our objective was to determine how this policy impacted cesarean delivery rate and time on labor and delivery. Methods: A retrospective chart review was conducted of singleton, cephalic pregnancies delivering between 39 and 41 weeks, during the 6 months before and after the policy change. Results: From January 20, 2021, to January 29, 2022, there were 386 eligible deliveries, 50.3% before and 49.7% after the policy change. There was no statistical reduction in total inductions after the policy change restricting eIOL (67.5% vs. 62.0%, P = 0.287). Time on labor and delivery was similar (20.8 ± 13.0 vs. 20.6 ± 11.7 h, P = 0.800). Cesarean delivery rates were unchanged (14.4% vs. 15.1%, P = 0.887). Conclusions: Induction rates were not impacted by the policy. This suggests physicians found alternative non-elective reasons for induction. There were no significant differences in time spent on labor and delivery or cesarean delivery rate. The study was a convenience sample and not powered for definitive outcome differences.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".