Impact of bariatric surgery on anthropometric, metabolic, and reproductive outcomes in polycystic ovary syndrome: a systematic review and meta‐analysis
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is a common endocrine disorder in females. Modest weight loss improves reproductive and metabolic PCOS features. While lifestyle modifications and pharmacotherapies remain first-line weight loss strategies, bariatric surgery is emerging as a potentially effective treatment. We performed a systematic review and meta-analysis of published literature to examine the impact of bariatric surgery in PCOS to inform the 2023 International PCOS Evidence-based Guidelines. Electronic databases were searched for observational studies and trials comparing pharmacologic or lifestyle treatments to bariatric surgery in women with PCOS or bariatric surgery in women with or without PCOS. Anthropometric, reproductive, hormonal, and metabolic outcomes were included and, where possible, meta-analyzed using random-effects models. Risk of bias and evidence quality were assessed. Ten studies were included involving 432 women with and 590 women without PCOS. Comparisons between bariatric surgery and pharmacologic or lifestyle treatments were only reported in one study each, and most reproductive outcomes were limited to a single study; therefore, meta-analyses could not be performed. Meta-analysis found that women with PCOS experience similar improvements in anthropometric, hormonal, and metabolic outcomes after bariatric surgery compared to those without PCOS. Existing research is limited and of low quality with high risk of bias, especially in comparison to existing PCOS treatments and with respect to reproductive outcomes including pregnancy, highlighting the need for additional studies to inform clinical recommendations.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".