Cognitive Function as a Predictor of Major Mobility Disability in Older Adults: Results From the LIFE Study.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Many cross-sectional studies have confirmed a link between gait speed and cognitive function. However, it is unknown whether cognitive function plays a role in the onset of major mobility disability (MMD) and if the effects are independent of physical function. This study examined cognitive and physical function as predictors of MMD across an average of 2.6 years of follow-up in community-dwelling older adults with compromised mobility. RESEARCH DESIGN AND METHOD: Data were collected from 1,635 participants in the Lifestyle Interventions and Independence for Elders (LIFE) study ages 70-89 years free of MMD at baseline. MMD was assessed every 6 months and defined as the inability to walk 400 m in ≤15 min without assistance or sitting. Cognitive function was assessed at baseline, 18 months, and 24 months using a cognitive battery categorized into four domains: global cognitive function, processing speed, verbal memory, and executive function. RESULTS: < .001) while other domains were not. DISCUSSION AND IMPLICATIONS: In the LIFE study, processing speed at baseline and follow-up was a significant predictor of subsequent MMD although the observed association may be explained by physical function as reflected in gait speed. More studies are needed to understand how cognitive function, alone and in combination with physical function, influences risk of MMD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".