Walking Speed Assessed by 4-Meter Walk Test in the Community-Dwelling Oldest Old Population in Vietnam
Bibliographic record
Abstract
This study aims to provide data on usual walking speed in individuals aged 80 years or older and determine the association between walking speed and related factors in community-dwelling older adults. A cross-sectional study design was conducted to measure walking speed on community-dwelling elders aged 80 years or older in Soc Son district, Vietnam. Walking speed was assessed by a 4-Meter Walk Test with a usual-pace walking mode. Health-related characteristics of participants including risk of falls (The Timed Up and Go test, activities of daily living (ADL), instrumental activities of daily living (IADLs), cognitive impairment (Mini-Cog test) and frailty syndrome (The Reported Edmonton Frail Scale (REFS)). Multiple logistic regression was used to analyze the association between a slow walking speed and selected factors. A total of 364 older people were recruited, and the majority were female (65.4%). The overall average walking speed was 0.83 ± 0.27 m/s. The proportion of participants with a slow walking speed (<0.8 m/s) was 40.4%. Multiple logistic regression analyses showed that age, female, high fall risk (assessed by TUG test), ADL/IADL dependence and frailty syndrome had a negative effect on slow walking speed in this population. The results could provide useful reference data for further investigations and measures in clinical practice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".