Trends in induction of labour, 1998–2007: A population‐based study
Bibliographic record
Abstract
BACKGROUND: Increasing rates of induction have been reported in the UK, the USA, Canada and Australia since the early 1990s; however, there is a lack of population-based studies on trends and pharmacological management of induction of labour. AIMS: To determine population trends in induction of labour and predictors of failed induction (in caesarean section, specifically for failure to progress with cervix dilation < or =3 cm). METHODS: Trends in induction were determined for women in NSW who laboured at > or = 32 weeks from 1998 to 2007 (N = 739 904). To determine the predictors of failed induction, 92 359 deliveries of live singletons for whom linked birth and hospital data were available (2001-2005) were examined using logistic regression analysis. RESULTS: The rate of induction increased over the decade from 25.3 to 29.1%; however, among those induced with prostaglandin alone, it decreased from 33.5 to 23.8%. Oxytocin alone was the most commonly used labour induction agent overall (51%) and in most population subgroups. The predictors of failed induction in both nullipara and multipara included increasing maternal age, pre-term and post-term birth and the use of prostaglandin or mechanical methods of induction (neither oxytocin nor prostaglandin). CONCLUSIONS: The pharmacological agents used for induction of labour have changed over the past decade. An important area for future research is to investigate how the dosage of oxytocin and prostaglandin affects pregnancy 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".