Validating the CANRISK prognostic model for assessing diabetes risk in Canada’s multi-ethnic population
Bibliographic record
Abstract
INTRODUCTION: Despite high rates of undiagnosed diabetes and prediabetes, suitable risk assessment tools for estimating personal diabetes risk in Canada are currently lacking. METHODS: We conducted a cross-sectional screening study that evaluated the accuracy and discrimination of the new Canadian Diabetes Risk Assessment Questionnaire (CANRISK) for detecting diabetes and prediabetes (dysglycemia) in 6223 adults of various ethnicities. All participants had their glycemic status confirmed with the oral glucose tolerance test (OGTT). We developed electronic and paper-based CANRISK scores using logistic regression, and then validated them against reference standard blood tests using test-set methods. We used area under the curve (AUC) summary statistics from receiver operating characteristic (ROC) analyses to compare CANRISK with other alternative risk-scoring models in terms of their ability to discern true dysglycemia. RESULTS: The AUC for electronic and paper-based CANRISK scores were 0.75 (95% CI: 0.73-0.78) and 0.75 (95% CI: 0.73-0.78) respectively, as compared with 0.66 (95% CI: 0.63-0.69) for the Finnish FINDRISC score and 0.69 (95% CI: 0.66-0.72) for a simple Obesity model that included age, BMI, waist circumference and sex. CONCLUSION: CANRISK is a statistically valid tool that may be suitable for assessing diabetes risk in Canada's multi-ethnic population. CANRISK was significantly more accurate than both the FINDRISC score and the simple Obesity model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".