ASSOCIATION OF LIFELONG EXPOSURE TO COGNITIVE RESERVE-ENHANCING FACTORS WITH DEMENTIA RISK
Bibliographic record
Abstract
Background and aims: We examined the association of cognitive reserve-related factors over the lifespan with the risk of dementia in a community-based cohort of older adults. Methods: Information on early-life education, socioeconomic status, work complexity at age 20; mid-life occupation attainment; and late-life leisure activities was collected in a cohort of non-demented community dwellers (n=602) aged 75+ residing in Stockholm, Sweden in 1987–1989. The cohort was followed up to 9 years (until 1996) to detect incident dementia cases. Participants who developed dementia three years after the baseline were excluded. Structural Equation Modelling was used to generate latent factors of cognitive reserve from early-, mid-, and late-life. Results: A reduced risk of dementia was associated with early (RR: 0.6; 95% CI: 0.4–0.9), adult (RR: 0.6; 95% CI: 0.4–0.9), and late life (RR: 0.5; 95% CI: 0.4–0.7) reserve-enhancing latent factors in separate multivariable Cox models. Late life (RR: 0.7; 95% CI: 0.5–0.9) and partially, midlife factors (RR: 0.7; 95% CI: 0.5–1.06) preserved their association, but the effect of early life factor was attenuated (RR: 0.8; 95% CI: 0.5–1.2) in mutually adjusted model. The risk declined progressively with cumulative exposure to reserve-enhancing latent factors, and having high reserve scores in all three periods was associated with the lowest risk of dementia (RR: 0.40; 95% CI: 0.20–0.81). Similar associations were detected among APOE ε4 allele carriers and noncarriers. Conclusions: Cumulative exposure to reserve-enhancing factors over the lifespan is associated with reduced risk of dementia in late life, even among individuals with genetic predisposition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".