Personalising exercise recommendations for healthy cognition and mobility in aging: time to address sex and gender (Part 1)
Bibliographic record
Abstract
Impaired cognition and mobility are common in older adults and they often coexist due to shared pathophysiology.1 Worldwide, one new case of dementia is detected every 3 s.2 Exercise improves cognitive function and reduces the risk of mobility disability and falls.3–5Leaders in physical activity and exercise research are aiming to delineate what (e.g., type, duration, frequency and intensity) exercise should be recommended and when (e.g., midlife versus late life) it is best done for promoting cognitive and mobility outcomes in healthy individuals and in those at risk for cognitive impairment. Two other key questions are how (e.g., neurotrophic factors, cardiovascular fitness) and for whom (e.g., biological sex, gender) does exercise benefit cognition and mobility. Understanding mediators (i.e., how) could help maximise gains by enhancing intervention elements that impact key mechanisms at lower cost and/or risk. Identifying moderators (e.g., who, when)—factors that either attenuate or amplify the effects of exercise—will enable precise recommendations for individuals with similar characteristics (i.e., subgroups).In this two-part editorial series, we focus on for whom factors that may moderate the effect of exercise on cognitive function and mobility outcomes. In Part 1, we focus on biological sex and gender. Biological sex is defined as the genetics, gonadal hormones and phenotype resulting from XX versus XY chromosomes. Gender refers to the social, environmental, cultural and behavioural factors that influence individual actions and experiences. In Part 2, …
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".