The incidence of dementia in Canada. The Canadian Study of Health and Aging Working Group.
Bibliographic record
Abstract
OBJECTIVE: To estimate the incidence of dementia, including AD, among Canadians aged 65 and over. METHODS: A 5-year cohort study of 10,263 seniors was undertaken, including community and institutional samples. The baseline study in 1991 identified 1,132 prevalent cases of dementia through screening and clinical examination. The remaining 9,131 cases formed the incidence study sample and were rescreened and selectively reexamined in 1996. Incident cases were diagnosed using established criteria. Incidence was estimated based on the 1991 population, and included data on those who died between the first and second phases of the study. RESULTS: Of the nondemented cohort who remained alive in 1996, 5,432 people in the community (88.3%) and 210 (91.3%) in the institutional sample participated in the incidence study. Nine hundred sixty incident cases were identified; the overall age-standardized incidence rates were 21.8 (women) and 19.1 (men) per 1,000 nondemented persons per year. This translates into 60,150 new cases of dementia per year in Canada. The logarithm of the rates rises linearly with age, but suggests a slight slowing of growth in incidence in the oldest age groups. CONCLUSIONS: Our incidence estimates lie toward the upper end of the range of incidence estimates found in other studies. Nonetheless, we calculate that several factors may have biased our estimates downward, suggesting that the incidence of dementia may be higher than many studies have reported.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".