How to assess functional status: A new muscle quality index
Bibliographic record
Abstract
Aging is associated with decreases in muscle mass, muscle strength and muscle power, with muscle strength declining at a higher rate than muscle mass, but at a lower rate than muscle power. This progressive mismatch suggests a deterioration of muscle "quality" that may lead to functional incapacities. Although it may be difficult to synthesize the concept of muscle quality, the aim of the present paper was to propose a clinical definition of muscle quality in regard to the functional status. Accordingly, the muscle strength or muscle power per unit of muscle mass ratios appear to be clinically relevant markers of muscle quality. Several mechanisms susceptible to influence these ratios have been described, among which age, gender, sex hormones, obesity, physical activity and fibrosis. Various methods to assess muscle quality in both the clinical and research fields have also been listed, with a particular interest for the tests used to measure muscle power. Finally, we proposed a clinical screening tool to detect individuals at risk of functional incapacities. Briefly, the muscle quality score is based on handgrip strength assessment by hand dynamometer, muscle mass measurement by bioelectrical analysis, and leg muscle power estimation using a chair stand test.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".