Role of exercise training in polycystic ovary syndrome: a systematic review and meta‐analysis
Bibliographic record
Abstract
Preliminary evidence suggests exercise in polycystic ovary syndrome (PCOS) may improve reproductive and cardiometabolic parameters. Our primary aim was to determine the impact of exercise training on reproductive health in women with PCOS. Our secondary aim was to determine the effect of exercise training on cardiometabolic indices. A systematic review of published literature was conducted using MEDLINE and EMBASE based on a pre-published protocol (PROSPERO CRD42017065324). The search was not limited by year. Randomized controlled trials, non-randomized controlled trials and uncontrolled trials that evaluated an exercise intervention in women with PCOS and reported reproductive outcomes were included. Reproductive outcomes were analysed semi-quantitatively and a meta-analysis was conducted for reported cardiometabolic outcomes. Of 517 screened abstracts, 14 studies involving 617 women with PCOS were included: seven randomized controlled trials, one non-randomized controlled trial and six uncontrolled trials. There were insufficient published data to describe the effect of exercise interventions on ovulation quantitatively, but semi-quantitative analysis suggested that exercise interventions may improve menstrual regularity, pregnancy and ovulation rates. Our meta-analysis found that exercise improved lipid profiles and decreased waist circumference, systolic blood pressure and fasting insulin. The impact of exercise interventions on reproductive function remains unclear. However, our meta-analysis suggests that exercise interventions may improve cardiometabolic profiles in women with PCOS.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".