Ethnic differences in prediabetes incidence among immigrants to Canada: a population-based cohort study
Bibliographic record
Abstract
Abstract Background Prediabetes appears to be increasing worldwide. This study examined the incidence of prediabetes among immigrants to Canada of different ethnic origins and the age at which ethnic differences emerged. Methods We assembled a cohort of Ontario adults (≥ 20 years) with normoglycemia based on glucose testing performed between 2002 and 2011 through a single commercial laboratory database (N = 1,772,180). Immigration data were used to assign ethnicity based on country of origin, mother tongue, and surname. Individuals were followed until December 2013 for the development of prediabetes, defined using either the World Health Organization/Diabetes Canada (WHO/DC) or American Diabetes Association (ADA) thresholds. Multivariate competing risk regression models were derived to examine the effect of ethnicity and immigration status on prediabetes incidence. Results After a median follow-up of 8.0 years, 337,608 individuals developed prediabetes. Using definitions based on WHO/DC, the adjusted cumulative incidence of prediabetes was 40% (HR 1.40, CI 1.38–1.41) higher for immigrants relative to long-term Canadian residents (21.2% vs 16.0%, p
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".