Does Executive Function Explain the IQ-Mortality Association? Evidence from the Canadian Study on Health and Aging
Bibliographic record
Abstract
OBJECTIVE: To assess the robustness of the association between intelligence quotient (IQ) and mortality in older adults and to examine whether or not the association can be explained by more specific cognitive processes, including individual differences in executive functioning. METHODS: We examined the associations among Full Scale IQ, individual IQ subtest scores, and 10-year mortality among older community-dwelling, adult participants in the Canadian Study of Health and Aging, who were verified as disease and cognitive-impairment free at baseline via comprehensive medical and neurological evaluation (n = 516). Survival analysis including Cox proportional hazards regression models were used to examine mortality risk as a function of Full Scale IQ and its specific subcomponents. RESULTS: An inverse association was found between IQ and mortality, but this did not survive adjustment for demographics and education. The association between IQ and mortality seemed to be predominantly accounted for by performance on one specific IQ subtest that taps executive processes (i.e., Digit Symbol (DS)). Performance on this subtest uniquely and robustly predicted mortality in both unadjusted and adjusted models, such that a 1-standard deviation difference in performance was associated with a 28% change in risk of mortality over the 10-year follow-up interval in adjusted models. CONCLUSIONS: The association between IQ and mortality in older adults may be predominantly attributable to individual differences in DS performance.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".