Labour assessment programs to delay admission to labour wards
Bibliographic record
Abstract
BACKGROUND: Application of specific criteria for diagnosis of active labour as part of a labour assessment program aims to differentiate more accurately between latent and active phases of labour. OBJECTIVES: The objective of this review was to assess the effects of the use of specific criteria by caregivers in diagnosing active labour in term pregnancy. SEARCH STRATEGY: We searched the Cochrane Pregnancy and Childbirth Group trials register and the Cochrane Controlled Trials Register. Date of last search: January 1998. SELECTION CRITERIA: Randomised trials comparing caregivers' application of strict diagnostic criteria for active labour with routine care. DATA COLLECTION AND ANALYSIS: Trial quality was assessed. MAIN RESULTS: One study of 209 women was included. The trial was of excellent quality. Women who experienced early labour assessment were less likely to receive intrapartum oxytocics than women who received standard care (odds ratio 0.45, 95% confidence interval 0.25 to 0.80) and analgesia (odds ratio 0.36, 95% confidence interval 0.16 to 0.78). They reported higher levels of control during labour and birth (weighted mean difference 16.00, 95% confidence interval 7.52 to 24.48). There were no differences detected for rate of caesarean section and other important measures of maternal and neonatal outcome. REVIEWER'S CONCLUSIONS: Early labour assessment (which includes use of specific criteria for diagnosis of active labour) may have some positive outcomes for women at term pregnancy.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".