Higher risk of type 2 diabetes in women with hyperandrogenic polycystic ovary syndrome
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of type 2 diabetes (T2D) in women with polycystic ovary syndrome (PCOS) in relation to body mass index (BMI) and the hyperandrogenic (HA) PCOS phenotype. DESIGN: Population-based cohort study. SETTING: Data from six Swedish national registers, with participants being followed for a maximum of 19 years. PATIENT(S): All women with an International Statistical Classification of Diseases and Related Health Problems, version 10, diagnosis of PCOS, androgen excess, or anovulatory infertility born between 1950 and 1999 (n = 52,535) were identified in the Patient Register. The HA PCOS phenotype was defined by two filled prescriptions for anti-androgenic drugs. For each woman with PCOS, five control women (n = 254,624) were randomly chosen from the Total Population Register, matched for age and geographic area. INTERVENTION(S): No interventions were performed. MAIN OUTCOME MEASURE(S): International Statistical Classification of Diseases and Related Health Problems, version 10, diagnosis of T2D or prescription of antidiabetic treatment other than metformin. RESULT(S): The cumulative incidence rates of T2D were 1.3%, 4.4%, and 14.2% in controls (non-PCOS women) and women with normoandrogenic (NA) and HA PCOS, respectively. After adjustment for BMI, women with PCOS had a twofold higher rate of T2D than non-PCOS women (adjusted hazard ratio, 2.52 [95% confidence interval, 2.15-2.96]). Women with HA PCOS had a higher rate of T2D than those with NA PCOS (adjusted hazard ratio, 3.86 [95% confidence interval, 3.16-4.72]). CONCLUSION(S): Polycystic ovary syndrome is an independent risk factor for T2D, even after adjustment for BMI. Women with the HA PCOS phenotype face an even higher risk of T2D than those with the NA PCOS phenotype.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".