Assessment of dysglycemia risk in the Kitikmeot region of Nunavut: using the CANRISK tool
Bibliographic record
Abstract
INTRODUCTION: The Public Health Agency of Canada adapted a Finnish diabetes screening tool (FINDRISC) to create a tool (CANRISK) tailored to Canada's multi-ethnic population. CANRISK was developed using data collected in seven Canadian provinces. In an effort to extend the applicability of CANRISK to northern territorial populations, we completed a study with the mainly Inuit population in the Kitikmeot region of Nunavut. METHODS: We obtained CANRISK questionnaires, physical measures and blood samples from participants in five Nunavut communities in Kitikmeot. We used logistic regression to test model fit using the original CANRISK risk factors for dysglycemia (prediabetes and diabetes). Dysglycemia was assessed using fasting plasma glucose (FPG) alone and/or oral glucose tolerance test. We generated participants' CANRISK scores to test the functioning of this tool in the Inuit population. RESULTS: A total of 303 individuals participated in the study. Half were aged less than 45 years, two-thirds were female and 84% were Inuit. A total of 18% had prediabetes, and an additional 4% had undiagnosed diabetes. The odds of having dysglycemia rose exponentially with age, while the relationship with BMI was U-shaped. Compared with lab test results, using a cut-off point of 32 the CANRISK tool achieved a sensitivity of 61%, a specificity of 66%, a positive predictive value of 34% and an accuracy rate of 65%. CONCLUSION: The CANRISK tool achieved a similar accuracy in detecting dysglycemia in this mainly Inuit population as it did in a multi-ethnic sample of Canadians. We found the CANRISK tool to be adaptable to the Kitikmeot region, and more generally to Nunavut.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".