Incidence and outcomes of diabetes mellitus in elderly people: report from the Canadian Study of Health and Aging.
Bibliographic record
Abstract
BACKGROUND: The epidemiology of diabetes in elderly people is not well understood. The purpose of this study was to estimate the incidence of diabetes mellitus among elderly people in Canada and the relative risks of death and admission to an institution among elderly diabetic patients. METHODS: The study was a secondary analysis of data for a community-dwelling sample from the Canadian Study of Health and Aging, a nationwide representative cohort study of 9008 elderly people (65 years of age or older at baseline) in Canada. Diabetes was identified primarily by self-reporting, and a clinician's diagnosis and the presence of treatments for diabetes were used to identify diabetic patients who did not report that they had the condition. RESULTS: The reliability of self-reported diabetes (the kappa statistic) was 0.85. The estimated annual incidence of diabetes was 8.6 cases per thousand for elderly Canadians. Incidence decreased with age, from 9.5 for subjects 65-74 years of age, to 7.9 for those 75-84 years of age and then to 3.1 for those 85 years of age and older. Diabetes was associated with death (relative risk 1.87, 95% confidence interval 1.59-2.19) and admission to an institution (relative risk 1.58, 95% confidence interval 1.28-1.94). INTERPRETATION: Diabetes mellitus is common among elderly people, but the incidence declines among the very old.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".