Additional file 1: of Ethnic differences in prediabetes incidence among immigrants to Canada: a population-based cohort study
Bibliographic record
Abstract
Table S1. Classification of ethnic groups by country of origin using the Immigration, Refugees and Citizenship Canada Permanent Resident (IRCC-PR) database. Classification of ethnic groups based on an algorithm using country of origin, followed by mother tongue and surnames using federal immigration and administrative data from 2002 to 2013. Table S2. Characteristics of immigrants in the study population, by ethnicity (Nâ =â 334,678). Sociodemographic characteristics of all immigrants in the cohort by ethnicity using federal immigration and administrative data from 2002 to 2013. Figure S1. Adjusteda cumulative incidence function for prediabetesb by immigration status, using glucose thresholds according to the World Health Organization and Diabetes Canada. Adjusted cumulative incidence function of prediabetes ascertained based on the WHO and DC definition of prediabetes among immigrants and long-term residents based on all available population-based data from 2002 to 2013. Figure S2. Association between ethnicity and adjusted prediabetes incidence among immigrants, by sex, using glucose thresholds according to the World Health Organization and Diabetes Canada. Adjusted cumulative incidence function of prediabetes ascertained based on the WHO and DC definition of prediabetes among immigrants of different ethnicities by sex using all available population-based data from 2002 to 2013. Figure S3. Adjusteda cumulative incidence function for prediabetesb by ethnicity, using glucose thresholds according to the American Diabetes Associationâ s definitionsc. Adjusted cumulative incidence function of prediabetes ascertained based on the American Diabetes Association definition of prediabetes among immigrants of different ethnicities using all available population-based data from 2002 to 2013. (DOCX 1684 kb)
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.317 | 0.015 |
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".