Original Contribution Is Complexity of Work Associated with Risk of Dementia? The Canadian Study of Health and Aging
Bibliographic record
Abstract
The authors evaluated the association of complexity of work with data, people, and things with the incidence of dementia, Alzheimer’s disease, and vascular dementia in the Canadian Study of Health and Aging, while adjusting for work-related physical activity. The Canadian Study of Health and Aging is a 10-year population study, from 1991 to 2001, of a representative sample of persons aged 65 years or older. Lifetime job history allowed application of complexity scores and classification of work-related physical activity. Analyses included 3,557 subjects, of whom 400 were incident dementia cases, including 299 with Alzheimer’s disease and 93 with vascular dementia. In fully adjusted Cox regression models, high complexity of work with people or things reduced risk of dementia (hazard ratios were 0.66 (95 % confidence interval: 0.44, 0.98) and 0.72 (95 % confidence interval: 0.52, 0.99), respectively) but not Alzheimer’s disease. For vascular dementia, hazard ratios were 0.36 (95 % confidence interval: 0.15, 0.90) for high complexity of work with people and 0.50 (95 % confidence interval: 0.25, 1.00) for high complexity of work with things. Subgroup analyses according to median duration (23 years) of principal occupation showed that associations with complexity varied according to duration of employment. High complexity of work appears to be associated with risk of dementia, but effects may vary according to subtype. Alzheimer disease; cohort studies; dementia; motor activity; occupations; risk factors; work
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".