Slow gait speed as a potential indicator for common geriatric syndromes besides sarcopenia
Bibliographic record
Abstract
Abstract Background Geriatric syndromes are posing increasing health threat in an aging population. The reliable indicator of geriatric syndromes is of great clinical value for early diagnosis and intervention. Aims To investigate the potential application of slow gait speed as a signal for identifying common geriatric syndromes besides sarcopenia in the elderly. Methods A total number of 985 elderly outpatients (457 men and 528 women) were recruited in the study. The subjects were classified into two groups according to the gait speed cut-off (1.0m/s), with the individuals being assigned as normal speed group (NSG) when the gait speed≥1.0m/s and the slow speed group (SSG) was defined as the gait speed <1.0m/s. CGA management system Simply Edition (CGA-SE) software was implemented to collect data, compare the demographic variations and assess the prevalence of functional decline in the two groups. Results Compared to the NSG, SSG subjects were significantly older, shorter in height, lighter in weight and consumed more medicine. SSG subjects also showed a higher score in Edmonton symptom assessment, Self-Rating Depression Scale (SDS), Self-rating Anxiety Scale (SAS), and Mini Nutritional Assessment (MNA), and had a lower score in Barthel index of Activities of Daily Living (BADL) assessment and Mini-Mental State Examination (MMSE). There was a significantly higher prevalence of frailty, disability, depression, and dementia in SSG when compared to NSG. In addition, gait speed was an independent predictive factor associated with a higher risk of frailty, disability, dementia, and swallowing dysfunction. Conclusions Slow gait speed could be used as an indicator for several common geriatric syndromes in elderly outpatients. We recommended the 6 meters walk test as a routine examination for the elderly in the geriatric department.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".