Diabetes risk in women with gestational diabetes mellitus and a history of polycystic ovary syndrome: a retrospective cohort study
Bibliographic record
Abstract
AIMS: To investigate whether polycystic ovary syndrome further increases postpartum diabetes risk in women with gestational diabetes mellitus and to explore relationships between polycystic ovary syndrome and incident diabetes in women who do not develop gestational diabetes. METHODS: This retrospective cohort study (Quebec Physician Services Claims; Hospitalization Discharge Databases; Birth and Death registries) included 34 686 women with gestational diabetes during pregnancy (live birth), matched 1:1 to women without gestational diabetes by age group, year of delivery and health region. Diagnostic codes were used to define polycystic ovary syndrome and incident diabetes. Cox regression models were used to examine associations between polycystic ovary syndrome and incident diabetes. RESULTS: Polycystic ovary syndrome was present in 1.5% of women with gestational diabetes and 1.2% of women without gestational diabetes. There were more younger mothers and mothers who were not of white European ancestry among those with polycystic ovary syndrome. Those with polycystic ovary syndrome more often had a comorbidity and a lower proportion had a previous pregnancy. Polycystic ovary syndrome was associated with incident diabetes (hazard ratio 1.52; 95% CI 1.27, 1.82) among women with gestational diabetes. No conclusive associations between polycystic ovary syndrome and diabetes were identified (hazard ratio 0.94; 95% CI 0.39, 2.27) in women without gestational diabetes. CONCLUSION: In women with gestational diabetes, polycystic ovary syndrome confers additional risk for incident diabetes postpartum. In women without gestational diabetes, an association between PCOS and incident diabetes was not observed. Given the already elevated risk of diabetes in women with a history of gestational diabetes, a history of both polycystic ovary syndrome and gestational diabetes signal a critical need for diabetes surveillance and prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".