Predictors of successful labor induction with oral or vaginal misoprostol
Bibliographic record
Abstract
OBJECTIVE: To identify independent predictors of successful labor induction with oral or vaginal misoprostol. METHODS: Women enrolled in four previous randomized trials involving oral or vaginal misoprostol for cervical ripening and labor induction were included in the present cohort study, with dosing of 25-50 microg every 4 to 6 h vaginally (n = 574) or 50 microg every 4 h orally (n = 207). Multiple logistic regression was performed to identify factors independently associated with successful labor induction -- defined as vaginal delivery within 12 h, vaginal delivery within 24 h and spontaneous vaginal delivery. Predictors of Cesarean birth and the need for only one dose of misoprostol were also identified. Variables included in the models were maternal age, weight, height, parity, gravidity, membrane status, route of misoprostol, gestational age, birth weight, and Bishop score and its individual components. RESULTS: Maternal age, height, weight, parity, birth weight, dilatation, effacement and cervical station were associated with vaginal delivery within 24 h of induction. Maternal age, height, weight, nulliparity, birth weight and route of misoprostol were associated with Cesarean birth, with oral misoprostol being associated with a lower rate of Cesarean birth. The need for only one dose of misoprostol was predicted by maternal height, weight, parity, gestational age, Bishop score and route of misoprostol. CONCLUSION: Characteristics of the woman (height, weight, parity), the fetus (birth weight) and some of the individual components of the Bishop score, were associated with successful labor induction, with oral misoprostol being associated with a lower rate of Cesarean birth.
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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 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".