The Prevalence of Low Handgrip Strength and Its Predictors among Outpatient Older Adults in a Tertiary Care Setting: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Low muscle strength is linked to several adverse health outcomes. However, there are limited data regarding its prevalence and associated factors in Thai older adults. This study aimed to fill that gap. METHODS: This cross-sectional study was conducted with patients aged ≥ 60 years at the outpatient clinic of the internal medicine department of a tertiary care hospital from April 2020 to December 2021. Patient characteristics were collected, and a handgrip dynamometer was used to measure handgrip strength (HGS). Low HGS was defined according to the 2019 recommendations of the Asian Working Group for Sarcopenia. RESULTS: In total, 198 patients were recruited. The prevalence of low HGS was 51%. Median HGS was 17.8 kg and 27.7 kg in women and men, respectively. Every age per year increase, greater number of medications of any type, and lower Montreal Cognitive Assessment (MoCA) score were independent factors associated with low HGS, with adjusted odds ratios of 1.1, 1.2, and 0.9, respectively. CONCLUSIONS: Low HGS was prevalent among older patients in this setting, indicating a high degree of possible sarcopenia. As there were some modifiable factors associated with low HGS, routine measurement, medication review, and cognitive evaluation are recommended for early diagnosis and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".