Bedside ultrasound measurement of skeletal muscle
Bibliographic record
Abstract
PURPOSE OF REVIEW: Skeletal muscle and lean body mass may be vital to prognosis and functional recovery in chronic and acute illness, particularly in conditions in which muscle atrophy is prevalent. Ultrasound provides a precise and expedient method to measure muscle mass and changes in skeletal muscle at the bedside. RECENT FINDINGS: Here, we describe the various methodological approaches along with the validation and reliability tests that have been performed in various populations. Current applications of ultrasound in chronic and acute illness as well as its limitations and strengths in quantifying the muscle mass and changes in muscle over time are discussed. To capitalize on the beneficial features of ultrasound for measuring muscle, we describe the work that is needed to optimize the usefulness of ultrasound in chronic disease and acute care. SUMMARY: Given the precision, practicality, and ease of use, ultrasound is emerging as a highly useful tool in expediently measuring the muscle mass and changes in muscle tissue at the bedside. Ultrasound may be valuable in identifying patients who are at risk of malnutrition, in tracking muscle atrophy for the purpose of calculating nutrient delivery, and in assessing the success or failure of nutrition, pharmacological and rehabilitative interventions that aim to counter muscle atrophy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".