High rates of the metabolic syndrome in a First Nations Community in western Canada: prevalence and determinants in adults and children.
Bibliographic record
Abstract
OBJECTIVES: Increasing type 2 diabetes in Aboriginal communities across North America raises concerns about metabolic syndrome in these populations. Some prevalence information for American Indians exists, but little has been available for Canada's First Nations. STUDY DESIGN: We screened 60% of the eligible population of a single First Nation in Alberta for diabetes, pre-diabetes, cardiovascular risk, and metabolic syndrome. METHODS: NCEP/ATP III and IDF criteria were used to identify metabolic syndrome in participants aged > or = 18; modified NCEP/ATP III criteria were used for participants aged < 18. Logistic regression identified factors associated with the metabolic syndrome. RESULTS: 297 individuals were screened (176 adults, 84 children/adolescents, with complete data). 52.3% of adults had metabolic syndrome using NCEP/ATP III criteria, and 50% using IDF criteria. 40.5% of individuals aged < 18 had the condition. Waist circumference was the most prevalent correlate. Bivariate analysis suggested that age, BMI, weight, Alc, LDL-C, ADA risk score and activity pattern were associated with metabolic syndrome. CONCLUSIONS: Our data represent the first available for Western Cree and are consistent with prevalence reported for Aboriginal populations in Ontario and Manitoba. High rates of obesity, pre-diabetes and metabolic syndrome for participants aged < 18 raise concerns about future prevalence of diabetes and cardiovascular disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".