Sex Matters: Association with Superager Classification and Risk Factors
Bibliographic record
Abstract
Superagers are 80 to 89-year-olds with average or better cognition and memory equivalent to individuals 20 to 30 years younger. As sex and modifiable lifestyle/health factors influence cognitive aging and dementia risk, we examined their impact on superager status. Data from participants (n = 469; 67% female) aged 80-89 years old were analyzed from an online database that included demographic and dementia risk factors, and performance on tasks assessing working memory, cognitive inhibition, associative memory, and set shifting. Cross-sectional comparisons were made between superagers and those with typical-for-age cognitive abilities (typical-agers) to examine relationships between sex, superager status, and dementia risk factors. Females performed better than age-matched males on the associative memory task in the 50-69 years old group used for normative comparisons, and in the 80-89 years old group (ps < .001). More females than males were classified as superagers using non-sex-stratified normative comparisons (p = .009), and in sex-stratified normative comparisons (p = .022). Total weighted dementia risk reduced odds of superager status (OR = 0.199, 95% CI [0.046, 0.829]). Other lifestyle dementia risk factors were unrelated to superager status or could not be tested due to low endorsement. The findings support observations that superaging is more common in females, even when controlling for sex differences in memory performance. Future studies of superagers should account for sex differences. Results support being ambitious about dementia prevention, as having fewer modifiable dementia risk factors may be positively associated with superager status.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".