Does cognitive status modify the relationship between education and mortality? Evidence from the Canadian Study of Health and Aging
Bibliographic record
Abstract
BACKGROUND: There is compelling evidence of an inverse relationship between level of education and increased mortality. In contrast to this, one study showed that among subjects with Alzheimer's Disease, those with high education are more than twice as likely to die earlier; however, this result has proven difficult to replicate. We examine the relationship between education and mortality by cognitive status, using a large, nationally representative sample of elderly people. PATIENTS: A representative sample of 10,263 people, aged 65 or over, from the 10 Canadian provinces, participated in the Canadian Study of Health and Aging in 1991. METHODS: Information about age, gender, education, and an initial screening for cognitive impairment were collected; those who screened positive for cognitive impairment were referred for a complete clinical and neuropsychological examination, from which cognitive status and clinical severity of dementia were assessed. Vital status and date of death were collected at follow-up in 1996. The analysis was conducted using survival analysis. RESULTS: Cognitive status modifies the relationship between education and mortality. For those with no cognitive impairment, an inverse relationship between education and mortality exists. Elderly people with cognitive impairment but no dementia, or those with dementia, are more likely to die early than the cognitively normal at baseline, but no relationship exists between education and mortality. INTERPRETATION: These findings do not support previous work that showed a higher risk of mortality among highly educated dementia subjects.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".