Risk factors and ramifications of failure to achieve cervical ripening with prostaglandins -- Retrospective Cohort Study
Bibliographic record
Abstract
Objective: To assess the characteristics and evaluate the outcomes of women who failed to respond to cervical ripening with prostaglandins. Methods: A retrospective cohort analysis (2012-2018) of all women with singleton gestation who underwent induction of labor, due to post-date pregnancy, with a slow-release prostaglandin-E2 vaginal insert for cervical ripening. Overall, 1285 women were divided into 2 groups: a) responders - 1,202 (93.54%) - achieved ripening within 24 hours ; b) non-responders- 83 (6.46%) – did not achieve cervical ripening within 24 hours. Characteristics and outcomes were compared between the groups. Primary outcome was defined as vaginal delivery rate following ripening process. Secondary outcomes were defined as composite adverse maternal and adverse neonatal outcomes. A model combining maternal characteristics and response rates to ripening was constructed as well. Results: In comparison to non-responders, responders achieved higher rates of vaginal delivery (96.51% vs. 66.27%, p<0.001). They also had lower rates of adverse maternal outcomes (12.81% vs. 24.10%, p=0.031) and of neonatal respiratory adverse outcomes (1.33% vs. 6.02%, p=0.009). The responders were also younger (30.03 vs 31.73, p=0.005), and less nulliparous (76.92% vs 50.99%, p<0.001). A multivariate analysis showed that failure to achieve a cervical ripening is an independent risk factor for intrapartum cesarean delivery due to failure to progress in labor (aOR 11.90, 95% CI 6.13-23.25). Conclusion: Women who achieve cervical ripening with PROPESS are younger, and more often multiparous. This group is associated with lower rates of intrapartum cesarean delivery and adverse outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".