Validation of Bedside Ultrasound of Muscle Layer Thickness of the Quadriceps in the Critically Ill Patient (VALIDUM Study)
Bibliographic record
Abstract
Background: In critically ill patients, muscle atrophy is associated with long‐term disability and mortality. Bedside ultrasound may quantify muscle mass, but it has not been validated in the intensive care unit (ICU). Here, we compared ultrasound‐based quadriceps muscle layer thickness (QMLT) with precise quantifications of computed tomography (CT)–based muscle cross‐sectional area (CSA). Methods: Patients ≥18 years old with abdominal CT scans performed for clinical reasons were recruited from 9 ICUs for an ultrasound assessment of the quadriceps. CT scans of the third lumbar vertebra, performed <24 hours before or <72 hours after ICU admission, were analyzed for CSA. Low muscularity was defined as 170 cm 2 for men and 110 cm 2 for women. The ultrasound probe was maximally compressed against the skin and QMLT was measured on 2 sites of each quadriceps <72 hours of the CT scan. Results: Mean CT‐derived muscle CSA was 109 ± 25 cm 2 for women and 168 ± 37 cm 2 for men, where 58% of patients exhibited low muscularity; only 2.7% patients were underweight according to body mass index. QMLT was positively correlated with CT CSA ( r = 0.45, P < .001). Based on logistic regression to predict low muscularity, QMLT independently generated a concordance index ( c ) of 0.67 ( P < .002), which increased to 0.77 ( P < .001) when age, sex, body mass index, Charlson Comorbidity Index, and admission type (surgical vs medical) were added. Conclusions: Our results suggest that QMLT alone with our current protocol may not accurately identify patients with low muscle mass.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".