Contrasting Bone Profiles in PCOS Are Related to BMI: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
CONTEXT: Controversial results have emerged regarding whether polycystic ovary syndrome (PCOS) is protective or increases the risk of bone frailty. OBJECTIVE: This study investigated whether the PCOS condition affects bone parameters of premenopausal women. This is an update for a previous meta-analysis published in 2019. DATA SOURCES: We searched MEDLINE and Embase. STUDY SELECTION: Studies were considered eligible for the update if published in English between October 1, 2018, and December 31, 2023. The diagnosis of PCOS should be based on National Institutes of Health criteria, the Rotterdam Consensus, Androgen Excess & PCOS Society criteria, or International Classification of Diseases codes in women over 18 years old. Only records with the Newcastle-Ottawa Scale ≥ 6 were selected for data extraction. DATA EXTRACTION: Data were extracted by 2 independent reviewers. DATA SYNTHESIS: We identified 31 studies that met the inclusion criteria for qualitative analysis from 3322 studies in the whole period (1990-2023). Overall, cross-sectional studies included 1822 individuals with PCOS and 1374 controls, while cohort studies incorporated 30 305 women with PCOS and 10,1907 controls. Contrasting profiles emerged after stratification using a body mass index (BMI) cutoff of 27 kg/m2. Individuals with PCOS and a BMI <27 kg/m2 exhibited lower vertebral and nonvertebral bone density, reduced bone turnover marker (osteocalcin), and increased bone resorption marker (C-terminal type I collagen) levels. Conversely, individuals with PCOS and a BMI ≥27 kg/m2 exhibited increased vertebral and nonvertebral bone mineral density, with no significant changes in bone formation and resorption markers (except osteocalcin). CONCLUSION: The findings of this study alert for a low bone mass, low bone formation, and increased bone resorption PCOS with a BMI <27 kg/m2.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".