Impact of prenatal exercise on maternal harms, labour and delivery outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To perform a systematic review of the relationships between prenatal exercise and maternal harms including labour/delivery outcomes. DESIGN: Systematic review with random effects meta-analysis and meta-regression. DATASOURCES: Online databases were searched up to 6 January 2017. STUDY ELIGIBILITY CRITERIA: Studies of all designs were included (except case studies) if they were published in English, Spanish or French and contained information on the population (pregnant women without contraindication to exercise), intervention (subjective or objective measures of frequency, intensity, duration, volume or type of exercise), comparator (no exercise or different frequency, intensity, duration, volume and type of exercise, alone ["exercise-only"] or in combination with other intervention components [e.g., dietary; "exercise + co-intervention"]) and outcome (preterm/prelabour rupture of membranes, caesarean section, instrumental delivery, induction of labour, length of labour, vaginal tears, fatigue, injury, musculoskeletal trauma, maternal harms (author defined) and diastasis recti). RESULTS: = 0 %). The remaining outcomes were not associated with exercise. Results from meta-regression did not identify a dose-response relationship between frequency, intensity, duration or volume of exercise and labour and delivery outcomes. SUMMARY/CONCLUSIONS: Prenatal exercise reduced the odds of instrumental delivery in the general obstetrical population. There was no relationship between prenatal exercise and preterm/prelabour rupture of membranes, caesarean section, induction of labour, length of labour, vaginal tears, fatigue, injury, musculoskeletal trauma, maternal harms and diastasis recti.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".