Diabetes among Inuit migrants in Denmark
Bibliographic record
Abstract
OBJECTIVES: The study aimed to estimate the prevalence of diabetes and impaired glucose intolerance (IGT) among Inuit migrants living in Denmark, and to compare with findings from Greenland. Further, we analyzed determinants for diabetes and impaired glucose metabolism. STUDY DESIGN: Cross-sectional, population-based epidemiological study. METHODS: This cross-sectional study included randomly selected Inuit migrants in Denmark aged 34 years and above. Diabetes and IGT were diagnosed using the oral glucose tolerance test. Body mass index (BMI) and waist circumference were measured, and blood samples were taken from each subject. Socio-demographic characteristics were investigated using a questionnaire. For comparison, data from the Greenland Population Study were used (n = 917). RESULTS: Of 506 eligible subjects, 256 (51%) participated. Twenty-six subjects had diabetes (10.2%) and twenty-eight had IGT (10.9%). Of those with diabetes, 64% had not been previously diagnosed. The prevalences of diabetes and IGT were not significantly different from those among Inuit in Greenland. Significant predictors of diabetes and impaired glucose metabolism (IGM) were found to be age, waist circumference and physical inactivity. The association between waist circumference and diabetes was significantly stronger among Inuit migrants in Denmark than among Inuit in Greenland. CONCLUSIONS: The prevalence of diabetes is high among the Inuit migrants in Denmark. However, unlike that reported in most studies, the prevalence was not significantly higher in the migrant population compared with the population of origin.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".