433International Journal of Circumpolar Health 68:5 2009 Diabetes in\tMétis\tsettlements ORIGINAL ARTICLE INCREASING RATES\tOF\tDIABETES\tAND CARDIOVASCULAR RISK\tIN\tMÉTIS SETTLEMENTS IN\tNORTHERN\tALBERTA
Bibliographic record
Abstract
Objectives. To determine the prevalence of diabetes (using secondary data analysis), as well as undiagnosed diabetes and pre-diabetes (using primary research methods) among adult Métis Settlement dwellers in northern Alberta. We also sought to identify cardiovascular risk factors. Study design. Quantitative research study utilizing both population census and community-based diabetes screening data. Methods. Self-reported diabetes was analyzed from the results of the Métis Settlement specific censuses in 1998 and 2006. Mobile clinics travelled into each of the 8 Métis Settlement communi-ties in Alberta recruiting 693 subjects for screening for undiagnosed diabetes, pre-diabetes and metabolic syndrome. Logistic regression analyses (adjusted for age and sex) were used to identify associated factors. Results. According to the censuses, 4,312 Métis individuals were living on Settlements in 1998 and 5,059 in 2006. Self-reported age-adjusted prevalence of diabetes increased significantly from 5.1 % in 1998 to 6.9 % in 2006 (p<0.01), with a crude prevalence increase of 66 % (p<0.01). In 2006, diabetes prevalence was higher among females than males, 7.8 % vs. 6.1 % respectively (p<0.05). Of the 266 adults screened in the fasting state, 5.3 % had undiagnosed diabetes, whereas 20.3%
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".