Education and Cognitive Aging: Accounting for Selection and Confounding in Linkage of Data From the Danish Registry and Survey of Health, Ageing and Retirement in Europe
Bibliographic record
Abstract
Earlier studies report inconsistent associations between education and cognitive aging. We assessed the association, accounting for selective dropout due to death or dementia, and, in a subsample, accounting for confounding by early-life intelligence. Data from the Danish component of the Survey of Health, Ageing and Retirement in Europe (n = 3,400) were linked to registry data (education records, dementia diagnoses, and mortality) and the Danish Conscription Database (youth intelligence measurements for 854 men). Word recall and verbal fluency were assessed up to 4 times over 10 years (2004-2013) and combined by averaging the z scores. We fitted a joint model linking a time-to-event model for dementia or death to a linear mixed-effects model for cognitive change. Rate of cognitive decline was slower among people with high education compared with low education (β = 0.112, 95% confidence interval (CI): 0.056, 0.170). Adjusting for youth intelligence did not attenuate the association between education and cognitive decline (crude β = 0.136, 95% CI: 0.028, 0.244 vs. adjusted β = 0.145, 95% CI: 0.022, 0.269). The results suggest that higher education may slow cognitive decline in later life. In this sample, results changed little when accounting for selective attrition and confounding by intelligence.
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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 | 0.124 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".