Diabetes and Impaired Glucose Tolerance Among the Inuit Population of Greenland
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of diabetes and impaired glucose tolerance (IGT) among the Inuit population of Greenland and to determine risk factors for developing glucose intolerance. RESEARCH DESIGN AND METHODS: This cross-sectional study included 917 randomly selected adult Inuit subjects living in three areas of Greenland. Diabetes and IGT were diagnosed using the oral glucose tolerance test. BMI and waist-to-hip ratio were measured and blood samples were taken from each subject. Sociodemographic characteristics were investigated using a questionnaire. RESULTS: The age-standardized prevalences of diabetes and IGT were 10.8 and 9.4% among men and 8.8 and 14.1% among women, respectively. Of those with diabetes, 70% had not been previously diagnosed. Significant risk factors for diabetes were family history of diabetes, age, BMI, and high alcohol consumption, whereas frequent intake of fresh fruit and seal meat were inversely associated with diabetic status. Age, BMI, family history of diabetes, sedentary lifestyle, and place of residence were significant predictors of IGT. CONCLUSIONS: The prevalence of diabetes is high among the Inuit of Greenland. Heredity was a major factor, while obesity and diet were important environmental factors. The high proportion of unknown cases suggests a need for increased diabetes awareness in Greenland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".