Elective induction of labour and maternal request: a national population‐based study
Bibliographic record
Abstract
OBJECTIVE: To estimate the rate of elective inductions in France and the proportion of them that were maternally requested, and to study the factors associated with elective inductions that were or were not requested by women. DESIGN: Cross-sectional population-based study. SETTING: All maternity units in France. POPULATION: About 14 681 women from the 2010 French National Perinatal Survey of a representative sample of births. METHODS: Inductions were classified as elective based on their indications and maternal and fetal characteristics, collected from medical records. Elective inductions requested by women were identified from the mother's postpartum interviews. Polytomous logistic regression analysis was used to study the determinants of inductions that were or were not maternally requested. Women with spontaneous labour served as the comparison group. MAIN OUTCOME MEASURE: Rate of elective inductions. RESULTS: The induction rate was 22.6, 13.9% elective. Among elective inductions, 47.3% were requested by women. The characteristics of mothers, pregnancies, and maternity units were similar in both groups of elective inductions. The main associated factors were parity 2 or more [adjusted odds ratio (OR) 4.7, 95% confidence interval (CI) 3.1-7.2 for maternally requested inductions and aOR of 1.8 (95% CI1.2-2.7) for unrequested inductions, compared with parity 0] and private hospital status [aOR 4.5 95% (CI 3.3-6.0) for maternally requested inductions and aOR 3.7 (95% CI 2.8-4.9) for inductions not requested by the mother]. We found no association between maternal social characteristics and type of elective induction. CONCLUSION: Parity and organisational factors appear to influence the decision about elective inductions. It would be interesting to determine how obstetricians and women make this decision and for what reasons. TWEETABLE ABSTRACT: About 13.9% of inductions of labour were elective in France, 47.3% of these requested by women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".