Use of Bedside Ultrasound to Assess Muscle Changes in the Critically Ill Surgical Patient
Bibliographic record
Abstract
BACKGROUND: Critical illness causes hypercatabolism, loss of lean body mass (LBM), and poor outcomes. Evaluating LBM in the critically ill is challenging, and it is uncertain whether nutrition support (NS) impacts LBM. This study measured quadriceps muscle layer thickness (QMLT) by bedside ultrasound (US) to estimate LBM changes in surgical intensive care unit (SICU) patients and healthy controls (HCs). METHODS: Trained RDNs measured QMLT via US at the midpoint and one-third distance between the superior margin of the patella and the anterior superior iliac spine. QMLT measurements were taken upon enrollment and repeated 1-2 times over 10 days. RESULTS: Fifty-two SICU patients and 15 HCs were enrolled. Average SICU percent QMLT loss per day at the midpoint and one-third landmarks was 3.2 ± 3.8 (P < 0.001) and 2.9 ± 5.7 (P = 0.001); and QMLT loss was higher between the second and third measurements (4.0 ± 8.0, P = 0.005 and 4.3 ± 9.8, P = 0.017 at the midpoint and one-third landmarks) compared with that at the first and second measurements (1.7 ± 9.2, P = 0.20 & 1.7 ± 9.4, P = 0.22). Changes were not associated with NS received. No significant QMLT change was found in HCs. CONCLUSIONS: SICU patients significantly lost QMLT over 10 days, with greater losses occurring after 5 days. These results support RDNs performing USs to detect QMLT changes and suggest this technique could be valuable to evaluate LBM changes in critically ill patients.
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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".