Past Gait Speed as an Independent Predictor of Mortality in Older Adults Beyond Current Gait Speed
Bibliographic record
Abstract
Abstract Background We investigated whether past values of gait speed in older adults provide additional prognostic information beyond current gait speed alone. We assessed various models to best describe past and current value for prediction. Methods We used data from the first five yearly rounds of the National Health and Ageing Trends Study, starting from 2011. The cohort consisted of 4289 community-dwelling participants aged 65 years and older. Gait speed was measured at baseline (Y1) and one year later (Y2). Three-year follow-up for mortality started in year 2. We estimated hazard ratios of various models using combinations of Y1 gait speed, Y2 gait speed, and change in gait speed from Y1 to Y2. Results The mean gait speed at year 2 was 0.77 m/s (0.26) and slightly increased by a mean of 0.04 m/s (0.20) from Y1 to Y2. A 0.1 m/s higher gait speed at Y2 was associated with decreased mortality (HR, 0.81 [0.78, 0.84]). Gait speed improvement from Y1 to Y2 decreased mortality (HR, 0.95 [0.92, 0.99] per 0.1 m/s increase). Models including both Y2 gait speed and change indicated that improvement in gait speed was associated with increased mortality (HR, 1.05 [1.00, 1.11]), independently of Y1 gait speed. Conclusions Past gait speed is predictive of mortality, independent of current gait speed, however, gait speed recovery does not completely negate mortality risks. Past gait speed information is a useful measure for risk prediction in older adults, but the direction of time is important for modelling and data interpretation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".