Bibliographic record
Abstract
Objective To determine the effect of support provided by doulas on the rate of medical interventions during labour for low-risk women intending to deliver vaginally at term. Data sources Comprehensive searches of the MEDLINE, EMBASE, and CINAHL databases were undertaken using the search terms labour support and doula . Study selection Randomized controlled trials evaluating the use of trained doulas for medical interventions during labour were selected and evaluated for methodologic quality. Articles of adequate quality were included in the synthesis. The outcomes of interest were rates of cesarean section, instrumental vaginal delivery, the use of oxytocin, and epidural anesthesia. Synthesis Outcomes were synthesized to determine overall odds ratios for relevant outcomes. Sensitivity analysis using only studies with high methodologic quality was completed, and publication bias was assessed. The presence and support of a trained doula reduced the odds of delivery by cesarean section and instrumental vaginal delivery. No significant effect was seen for the use of epidural anesthesia or the rates of oxytocin use. There was considerable heterogeneity among the studies. Conclusion Trained doulas help to reduce the odds of certain medical interventions during labour for low-risk women delivering at term.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".