Predicting 3-Year Incident Mobility Disability in Middle-Aged and Older Adults Using Physical Performance Tests
Bibliographic record
Abstract
OBJECTIVE: To identify a standard physical performance test, which can predict 3-year incident mobility disability independent of demographics. DESIGN: Longitudinal cohort study. SETTING: Population-based middle-aged and older adult cohort assessment performed at a local geriatric clinical center. PARTICIPANTS: Community-living middle-aged and older persons (age, 50-85y) without baseline mobility disability (N=622). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Mobility disability was ascertained at baseline and at 3-year follow-up using an established self-report method: self-reported inability to walk a quarter mile without resting or inability to walk up a flight of stairs unsupported. Physical performance tests included self-selected usual gait speed, time required to complete 5 times sit-to-stand (5TSTS), and 400-m brisk walking. Demographic variables age, sex, height, and weight were recorded. RESULTS: Overall, 13.5% participants reported 3-year incident mobility disability. Usual gait speed <1.2m/s, requiring >13.6 seconds to complete 5TSTS, and completing 400m at <1.19m/s walking speed were highly predictive of future mobility disability independent of demographics. CONCLUSIONS: Inability to complete 5TSTS in <13.7 seconds can be a clinically convenient guideline for monitoring and for further assessment of middle-aged and older persons, in order to prevent or delay future mobility disability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".