How do health and biological age influence chronological age and sex differences in cognitive aging: Moderating, mediating, or both?
Bibliographic record
Abstract
Much research on cognitive competence in normal older adults has documented age and sex differences. The authors used new cross-sectional data from the Victoria Longitudinal Study (VLS) (n=386; age 61 to 95 years) to examine how health and biological age influence age and sex differences in cognitive aging. The authors found evidence for both moderating and mediating influences. Age differences were moderated by health status, such that the negative effects of age were most pronounced among participants of relatively better health. Sex differences were moderated by health and were more pronounced among participants reporting comparatively poorer health. Although health mediated a notable amount of age-related cognitive variation, BioAge mediated considerably more variance, even after statistical control for differences in health. A complex pattern emerged for the mediation of sex differences: Although BioAge accounted for sex-related variation in cognitive performance, health operated to suppress these differences. Overall, both health and BioAge predicted cognitive variation independently of chronological age.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".