Assessing Dysglycemia Risk Among Younger Adults: A Validation of the Canadian Diabetes Risk Questionnaire
Bibliographic record
Abstract
OBJECTIVES: The Canadian prevalence and incidence of diabetes has increased by the greatest extent in young adulthood. The original Canadian Diabetes Risk Questionnaire (CANRISK) was created to assess dysglycemia risk among adults ≥40 years of age, but it has not been validated among younger adults. Furthermore, it is unclear whether a young adult-specific risk score would better identify dysglycemia in this age group. METHODS: Analyses were done on participants who completed the self-administered CANRISK and underwent anthropometric and blood glucose measurements, were 18 to 39 years of age, were not pregnant and had no previous diabetes diagnosis. A risk model was generated from a lenient stepwise function fit with predictors identified through univariate analyses. Risk scores were produced from adjusted odds ratios. Model performance was internally validated using bootstrap methods and compared with the original CANRISK prognostic tool. RESULTS: Of the 3,334 participants included in the study, 194 (5.8%) and 51 (1.5%) were living with prediabetes or undiagnosed diabetes, respectively. The model displayed an area under the curve of 73.0%, adjusted to 72.9% after bootstrapping; however, using the original CANRISK model resulted in similar results (area under the curve, 71.4%). Sensitivity and specificity of the new and original models were also comparable (78.8% vs 77.1% and 54.0% vs 55.0%, respectively). CONCLUSIONS: The original CANRISK score performed well among young adults, even when compared with a young adult-specific model. We suggest that the cut point be lowered for young adults and the tool be permitted for use in this age group.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".