The Underappreciated Role of Low Muscle Mass in the Management of Malnutrition
Bibliographic record
Abstract
Preserving muscle is not only crucial for maintaining proper physical movement, but also for its many metabolic and homeostatic roles. Low muscle mass has been shown to adversely affect health outcomes in a variety of disease states (eg, chronic obstructive pulmonary disease, cancer, cardiovascular disease) and leads to an increased risk for readmission and mortality in hospitalized patients. Low muscle mass is now included in the most recent diagnostic criteria for malnutrition. Current management strategies for malnutrition may not prioritize the maintenance and restoration of muscle mass. This likely reflects the challenge of identifying and measuring this body composition compartment in clinical practice and the lack of awareness by health care professionals of the importance that muscle plays in patient health outcomes. As such, we provide a review of current approaches and make recommendations for managing low muscle mass and preventing muscle loss in clinical practice. Recommendations to assist the clinician in the optimal management of patients at risk of low muscle mass include the following: (1) place muscle mass at the core of nutritional assessment and management strategies; (2) identify and assess low muscle mass; (3) develop a management pathway for patients at risk of low muscle mass; (4) optimize nutrition to focus on muscle mass gain versus weight gain alone; and (5) promote exercise and/or rehabilitation therapy to help maintain and build muscle mass. The need to raise awareness of the importance of screening and managing 'at risk' patients so it becomes routine is imperative for change to occur. Health systems need to drive clinicians to treat patients with this focused approach, and the economic benefits need to be communicated to payers. Lastly, further focused research in the area of managing patients with low muscle mass is warranted.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".