The utility of the ultrasonographic assessment of the lower leg muscles to evaluate sarcopenia and muscle quality in older adults
Bibliographic record
Abstract
Abstract Background The assessment of muscle quality is expected to help predict the prognosis of sarcopenia and examine the response to intervention. Ultrasonography can be used to evaluate approaches for determining muscle quantity and quality. We focused on the lower leg muscles and examined the relationship between the ultrasonographic assessments and the components of sarcopenia and muscle quality by comparing them with those of the quadriceps muscle (QFM). Methods 47 physically healthy older participants aged 78.3 ± 6.0 years (53% male) were enrolled in this cross‐sectional study. Muscle thickness (MT) and echo intensity (EI) of the lower leg muscles and QFM were assessed with ultrasonography. Muscle mass, grip strength, and gait speed, and lower leg muscle strength were measured. Muscle quality was calculated using a formula: leg muscle strength/leg muscle mass. Results The MTs and EIs of the tibialis anterior muscle (TA) and QFM were significantly associated with grip and leg strength. We observed a significant correlation in the MTs and EIs of the lower leg muscles and QFM. The EIs of the lower leg muscles and QFM showed significant negative correlations with muscle quality. In the multiple linear regression model, the EI of the TA and QFM was extracted as an independent factor of muscle quality (TA: β = −0.35, p = 0.0358; QFM: β = −0.30, p = 0.0327). Conclusions The ultrasonographic assessments of the lower leg muscles, especially the TA, were associated with sarcopenia components and muscle quality equal to or greater than those of the QFM.
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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 | 0.005 |
| 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".