Reliability of skeletal muscle ultrasound in critically ill trauma patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the safety and feasibility of the ultrasound assessment of quadriceps in the emergency setting. To assess the intra- and interrater reliability for the acquisition and analysis of ultrasound images of muscle thickness and echogenicity in critically ill trauma patients between health professionals with different levels of expertise. METHODS: Diagnostic accuracy study. Two examiners (expert and novice) acquired ultrasound images from ten patients; an experienced, blinded analyst quantified the images. In a separate group of ten patients, two analysts (expert and novice) quantified quadriceps muscle thickness and echogenicity (square or trace method) from images acquired by one examiner. RESULTS: Excellent reliability was found for image acquisition and analysis (intraclass correlation coefficients > 0.987; p < 0.001). The standard error of the measurement values ranged from 0.01 - 0.06cm for muscle thickness and from 0.75 - 2.04 arbitrary units for muscle echogenicity. The coefficients of variation were < 6% for thickness and echogenicity. The echogenicity values were higher when using the square technique than when using the tracing technique (p = 0.003). CONCLUSION: Ultrasound is safe, feasible, and reliable for muscle assessment in critically ill trauma patients, regardless of the assessor's level of expertise.
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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 | 0.007 | 0.053 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".