Comparison of Ultrasound‐Derived Muscle Thickness With Computed Tomography Muscle Cross‐Sectional Area on Admission to the Intensive Care Unit: A Pilot Cross‐Sectional Study
Bibliographic record
Abstract
Abstract Introduction The development of bedside methods to assess muscularity is an essential critical care nutrition research priority. We aimed to compare ultrasound‐derived muscle thickness at 5 landmarks with computed tomography (CT) muscle area at intensive care unit (ICU) admission. Secondary aims were to (1) combine muscle thicknesses and baseline covariates to evaluate correlation with CT muscle area and (2) assess the ability of the best‐performing ultrasound model to identify patients with low CT muscle area. Methods Adult patients who underwent CT scanning at the third lumbar area <72 hours after ICU admission were prospectively recruited. Muscle thickness was measured at mid‐upper arm, forearm, abdomen, and thighs. Low CT muscle area was determined using published cutoffs. Pearson correlation compared ultrasound‐derived muscle thickness and CT muscle area. Linear regression was used to develop ultrasound prediction models. Bland‐Altman analyses compared ultrasound‐predicted and CT‐measured muscle area. Results Fifty ICU patients were enrolled, aged 52 ± 20 years. Ultrasound‐derived muscle thickness at each landmark correlated with CT muscle area ( P < .001). The sum of muscle thickness at mid‐upper arm and bilateral thighs, including age, sex, and the Charlson Comorbidity Index, improved the correlation with CT muscle area ( r = 0.85; P < .001). Mean difference between ultrasound‐predicted and CT‐measured muscle area was −2 cm 2 (95% limits of agreement, −40 cm 2 to +36 cm 2 ). The best‐performing ultrasound model demonstrated good ability to identify 14 patients with low CT muscle area (area under curve = 0.79). Conclusion Ultrasound shows potential for assessing muscularity at ICU admission (Clinicaltrials.gov NCT03019913).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".