Muscle quality indices separately associate with joint-level power-related measures of the knee extensors in older males
Bibliographic record
Abstract
Abstract Purpose The purpose of this study was to investigate associations of muscle quality indices with joint-level power-related measures in the knee extensors of thirty-two older males (65–88 years). Methods Muscle quality indices included: echo intensity, ratio of intracellular- to total water content (ICW/TW), and specific muscle strength. Echo intensity was acquired from the rectus femoris (EIRF) and vastus lateralis (EIVL) by ultrasonography. ICW/TW was computed from electrical resistance of the right thigh obtained by bioelectrical impedance spectroscopy. Specific muscle strength was determined as the normalized maximal voluntary isometric knee extension (MVIC) torque to estimated knee extensor volume. Isotonic maximal effort knee extensions with a load set to 20% MVIC torque were performed to obtain the knee extension power-related measures (peak power, rate of power development [RPD], and rate of velocity development [RVD]). Power and RPD were normalized to MVIC. Results There were no significant correlations between muscle quality indices except between EIRF and EIVL (|r|≤ 0.253, P ≥ 0.162). EIRF was negatively correlated with normalized RPD and RVD (r ≤ − 0.361, P ≤ 0.050). ICW/TW was positively correlated with normalized peak power (r = 0.421, P = 0.020). Specific muscle strength was positively correlated with absolute peak power and RPD (r ≥ 0.452, P ≤ 0.012). Conclusion Knee extension power-related measures were lower in participants with higher EI, lower ICW/TW, and lower specific muscle strength, but the muscle quality indices may be determined by independent physiological characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".