Hand Grip Strength as a Predictive Tool for Upper Extremity Functionality, Balance, and Quality of Life in People With Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Upper extremity strength and function are rarely assessed in routine multiple sclerosis (MS) care. This study aimed to evaluate hand muscle strength and functionality in individuals with MS and investigate correlations with upper extremity function, cognitive status, health-related quality of life (HRQOL), and balance. METHODS: A cross-sectional study was conducted with 45 consecutive individuals with MS between the ages of 18 and 65. Upper limb motor strength was evaluated using a hand grip strength dynamometer. Upper limb functional capacity was assessed using the Nine-Hole Peg Test (9HPT) and the Duruoz Hand Index (DHI). Balance, coordination, and falls were measured with the Berg Balance Scale (BBS), Falls Efficacy Scale (FES), and the 30-Second Chair Stand Test (30CST). Cognitive function was evaluated using the Montreal Cognitive Assessment instrument and the Symbol Digit Modalities Test. Level of HRQOL was assessed using the self-reported 54-item MS Quality of Life-54 questionnaire. RESULTS: Out of the 45 participants (80% women, mean age 36.6 ± 8.6 years), higher hand grip dynamometer measures were strongly correlated with better DHI, 9HPT, BBS, FES, and 30CST scores. In the regression analysis, a 1-unit increase in dynamometer measures led to a 0.383 increase in overall HRQOL score. CONCLUSIONS: This study demonstrates that increased hand grip strength (HGS) is associated with better hand functionality, balance, and HRQOL in individuals with MS. It provides evidence to support more systematic measurement of HGS in the care of people with MS.
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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 | 0.004 |
| 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 | 0.001 | 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".