Advances in muscle health and nutrition: A toolkit for healthcare professionals
Bibliographic record
Abstract
Low muscle mass and malnutrition are prevalent conditions among adults of all ages, with any body weight or body mass index, and with acute or chronic conditions, including COVID-19. This article synthesizes the latest research advancements in muscle health and malnutrition, and their impact on immune function, and clinical outcomes. We provide a toolkit of illustrations and scientific information that healthcare professionals can use for knowledge translation, educating patients about the importance of identifying and treating low muscle mass and malnutrition. We focus on the emerging evidence of mitochondrial dysfunction in the context of aging and disease, as well as the cross-talk between skeletal muscle and the immune system. We address the importance of myosteatosis as a component of muscle composition, and discuss direct, indirect and surrogate assessments of muscle mass including ultrasound, computerized tomography, deuterated creatine dilution, and calf circumference. Assessments of muscle function are also included (handgrip strength, and physical performance tests). Finally, we address nutrition interventions to support anabolism, reduce catabolism, and improve patient outcomes. These include protein and amino acids, branched-chain amino acids, with a focus on leucine; β-hydroxy-β-methylbutyrate (HMB), vitamin D; n-3 polyunsaturated fatty acids (n-3 PUFA), polyphenols, and oral nutritional supplements. We concluded with recommendations for clinical practice and a call for action on research focusing on evaluating the impact of body composition assessments on targeted nutrition interventions, and consequently their ability to improve patient outcomes.
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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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.035 | 0.029 |
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".