165-LB: Development of Subsequent Diabetes Mellitus after Gestational Diabetes among Immigrant Women: Population-Based Cohort Study
Bibliographic record
Abstract
Objective: To evaluate which immigrant groups to Canada are at increased risk of diabetes (DM) after gestational diabetes (GDM), including the influence of time since migration. Research Design and Methods: This retrospective cohort study used administrative data within a universal healthcare system. Included were women with GDM who had given birth in Ontario, Canada, 2006-2014. Women from each World region were compared to native-born/long-term residents of Canada for risk of DM using Cox regression. Results were further evaluated by time since immigration (0-5, 5-10, 10-20 years vs. native-born/long-term residents [referent]), adjusting for age at index pregnancy, income, BMI, parity and any hypertensive disorder of pregnancy. Results: A total of 8630 women with GDM were followed for a median of 4.9 years. Relative to native-born/long-term residents (incidence rate 29.2 person-years), the age-adjusted hazard ratios (aHR) for DM were higher among women from South Asia (1.28, 95% confidence interval, CI 1.20-1.36), Latin America/the Caribbean (1.42, 95% CI 1.28-1.57), and Sub-Saharan Africa (1.59, 95% CI 1.39-1.83). Relative to native-born/long-term residents, the aHR for DM were 1.42 (95% CI 1.20-1.67) for those residing 0-5 years, 1.19 (95% CI 1.00-1.42) 5-10 years, and 1.41 (95% 1.20-1.65) 10-20 years since immigration. Conclusions: Certain broadly-defined immigrant groups are at a significantly higher future risk of developing diabetes after having GDM, especially those who are recent immigrants. Disclosure J. S. S. Ho: None. L. Lipscombe: None. S. H. Read: Employee; Self; Certara. L. Rosella: None. H. Berger: None. D. Feig: Advisory Panel; Self; Novo Nordisk. K. Fleming: None. J. G. Ray: None. B. R. Shah: None. S. Sarma: None.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".