Assessment of skeletal muscle mass in critically ill patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Low muscularity and skeletal muscle atrophy are commonly exhibited in critically ill patients and have major implications on patient outcomes. Typically, in the ICU, body composition is assessed through anthropometrics or bioelectrical impedance analysis, but these modalities cannot specifically quantify skeletal muscle; thus, we evaluate the merits and challenges of using computed tomography (CT) and ultrasonography to specifically measure skeletal muscle in the ICU. RECENT FINDINGS: CT-based cut points have been used to identify critically ill patients with low muscle mass, and low muscularity associates with poor clinical outcomes and function. Ultrasonography is emerging as a useful tool to quantify skeletal muscle loss and degradation in architecture, as well as prospectively track changes in these parameters over time. Rates of muscle atrophy and changes in muscle architecture has been quantified by ultrasonography and associated with poor clinical outcomes, but identification of critically ill patients with low muscularity is still in its infancy. SUMMARY: CT imaging and ultrasonography require additional comprehensive validations against accurate measures of whole body muscle mass. As these validations begin to emerge, there will be a need to translate this knowledge into a simple tool that clinicians can apply as part of routine care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".