Gait Velocity as a Single Predictor of Adverse Events in Healthy Seniors Aged 75 Years and Older
Bibliographic record
Abstract
PURPOSE: Although gait velocity (GV) measurement could predict poor outcomes, few studies regarding its usefulness as a single test in well functioning elderly persons have been pursued. The aim of this study was to asses whether GV could be sufficient to predict adverse events such as hospitalization for any cause, requirement for a caregiver, nursing home placement, falls, fractures, or death in healthy elderly persons. METHODS: Ours was a cohort study comprising 102 well functioning participants aged 75 and older. Demographic features, health status, and functional capacity were assessed at baseline and followed for adverse outcomes. Measurements included evaluation of cognition, activities of daily living, and mobility. The time required to walk the middle 8 meters of 10 meters was defined as GV. Three GV groups were distinguished: high GV (>1.1 m/s), median GV (1-0.7 m/s), and low GV (<0.7 m/s). RESULTS: At baseline, the three groups were comparable in their health status with an average age of 79.6 +/- 4 years. At 24 months, the low GV group had a significantly higher incidence of adverse events than did the other groups. Low GV was a predictor of hospitalization (relative risk [RR] = 5.9, 95% confidence interval [CI], 1.9-8.5), requirement of a caregiver (RR = 9.5, 95% CI, 1.3-2.5), and new falls (RR = 5.4, 95% CI, 2.0-4.3). These associations remained significant after a multiple logistic regression analysis. CONCLUSIONS: GV measurement in the ambulatory setting may allow the detection of healthy elderly people at risk for adverse events. These data may suggest that simple assessment of GV is enough to predict adverse events in well functioning older persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".