Slow gait speed could be a signal for common geriatric syndromes besides sarcopenia
Bibliographic record
Abstract
Abstract Background In China geriatricians is inadequate for the growing elderly. We need more doctors to recognize geriatric syndromes with a simple examination, which could help more elderly have better care.Aims Clarified that slow gait speed could be a signal to identify common geriatric syndromes besides sarcopenia in elderly outpatients.Methods According to the gait speed cut-off (< 1.0m/s), we classified 985 elderly outpatients (457 men and 528 women) into two groups. The groups were defined as the normal speed group (NSG, gait speed ≥ 1.0m/s), and the slow speed group (SSG, gait speed < 1.0m/s). We used the CGA management system Simply Edition (CGA-SE) software to collect data and compared demographic variations and the prevalence of functional decline in the two groups.Results Participants in SSG were significantly older, shorter in height, lighter in weight, used more drugs, and had a higher score in Edmonton, SDS, SAS, and MNA, and a lower score in BADL, and MMSE than in NSG. And they had significantly higher prevalence percentages in frailty, disability, depression, and dementia than in NSG. In addition, gait speed was an independent protective factor associated with frailty, disability, dementia, and swallowing dysfunction. And slow gait speed was an independent risk factor associated with frailty, depression, and dementia. Furthermore, participants with comorbidity, better function in daily life, and good cognition usually had faster gait speeds. However, participants with polypharmacy, low education, malnutrition risk, and frailty usually had slow gait speed(p < 0.05).Discuss Clinical physicians should give more attention to those with slow gait speed elderly outpatients, and be alert to their geriatric syndromes.Conclusions Slow gait speed could be a signal for several common geriatric syndromes in elderly outpatients. We recommended 6 meters gait speed test as a routine examination for the elderly, not only in the geriatric department but also others serving the elderly.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".