Validation of the Canadian Diabetes Risk Questionnaire Tool Among Young African, Caribbean, and Black Adults Living in Canada
Bibliographic record
Abstract
OBJECTIVES: The prevalence of type 2 diabetes (T2D) has increased substantially over the last decade among young populations living in Canada, with certain ethnic groups, such as African, Caribbean, and Black (ACB), being disproportionately affected. However, little is known about the suitability of the widely adopted T2D risk assessment tool, Canadian Diabetes Risk Questionnaire (CANRISK), for this population. METHODS: Two analytical samples of individuals aged 18 to 39 years were identified from 3 phases of CANRISK validation data collection: 1) young individuals from ACB ethnicities; and 2) all young individuals. Descriptive analyses were conducted for all predictors from the CANRISK and for prevalent dysglycemia (assessed by oral glucose tolerance test). The discriminatory ability of the CANRISK model was assessed by generating the area under the curve (AUC) and other model metrics. In addition, we assessed several cut points for the ACB subgroup to identify the optimal threshold. RESULTS: The prevalence of dysglycemia was higher among the ACB subgroup (n=715) than the overall young sample (n=3,960) (8.4% vs 7.7%). The CANRISK model performance was comparable for both groups, but slightly more accurate in the ACB subgroup (AUC 71.9% vs AUC 71.5%). The optimal cutoff for young ACB individuals was the same optimal cutoff of 19 as for the general Canadian youth population. CONCLUSIONS: Our findings indicate that the CANRISK tool, with a modified cutoff point, is a suitable tool to identify T2D risk among young adults from ACB ethnicities. Future studies could explore the tool's predictive ability via longitudinal studies to assess long-term diabetes risk.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".