Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND Polycystic ovary syndrome (PCOS) is a common condition in reproductive-aged women associated with impaired glucose tolerance (IGT), type 2 diabetes mellitus (DM2) and the metabolic syndrome. METHODS A literature search was conducted (MEDLINE, CINAHL, EMBASE, clinical trial registries and hand-searching) identifying studies reporting prevalence or incidence of IGT, DM2 or metabolic syndrome in women with and without PCOS. Data were presented as odds ratio (OR) [95% confidence interval (CI)] with fixed- and random-effects meta-analysis by Mantel-Haenszel methods. Quality testing was based on Newcastle-Ottawa Scaling and The Cochrane Collaboration's risk of bias assessment tool. Literature searching, data abstraction and quality appraisal were performed by two investigators. RESULTS A total of 2192 studies were reviewed and 35 were selected for final analysis. Women with PCOS had increased prevalence of IGT (OR 2.48, 95% CI 1.63, 3.77; BMI-matched studies OR 2.54, 95% CI 1.44, 4.47), DM2 (OR 4.43, 95% CI 4.06, 4.82; BMI-matched studies OR 4.00, 95% CI 1.97, 8.10) and metabolic syndrome (OR 2.88, 95% CI 2.40, 3.45; BMI-matched studies OR 2.20, 95% CI 1.36, 3.56). One study assessed IGT/DM2 incidence and reported no significant differences in DM2 incidence (OR 2.07, 95% CI 0.68, 6.30). One study assessed conversion from normal glucose tolerance to IGT/DM2 (OR 2.4, 95% CI 0.7, 8.0). No studies reported metabolic syndrome incidence. CONCLUSIONS Women with PCOS had an elevated prevalence of IGT, DM2 and metabolic syndrome in both BMI and non-BMI-matched studies. Few studies have determined IGT/DM2 or metabolic syndrome incidence in women with and without PCOS and further research is required.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| 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".