Bedside Ultrasound Is a Practical and Reliable Measurement Tool for Assessing Quadriceps Muscle Layer Thickness
Bibliographic record
Abstract
BACKGROUND: Critically ill patients commonly experience skeletal muscle wasting that may predict clinical outcome. Ultrasound is a noninvasive method that can measure muscle quadriceps muscle layer thickness (QMLT) and subsequently lean body mass (LBM) at the bedside. However, currently the reliability of these measurements are unknown. The objectives of this study were to evaluate the intra- and interreliability of measuring QMLT using bedside ultrasound. METHODS: Ultrasound measurements of QMLT were conducted at 7 centers on healthy volunteers. Trainers were instructed to perform measurements twice on each patient, and then a second trainee repeated the measurement. Intrarater reliability measured how consistently the same person measured the subject according to intraclass correlation (ICC). Interrater reliability measured how consistently trainer and trainee agreed when measuring the same subject according to the ICC. RESULTS: We collected 42 pairs of within operator measurements with an ICC of .98 and 78 pairs of trainer-to-trainee measurements with an ICC of .95. There were no statistically significant differences between the trainer and trainee results (trainer and trainee mean = -0.028 cm, 95% CI = -0.067 to -0.011, P = .1607). CONCLUSIONS: Excellent intra- and interrater reliability for ultrasound measurements of QMLT in healthy volunteers was observed when performed by a range of providers with no prior ultrasound experience, including dietitians, nurses, physicians, and research assistants. This technique shows promise as a method to evaluate LBM status in ICU or hospital settings and as a method to assess the effects of nutrition and exercise-based interventions on muscle wasting.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".