QUADRICEPS MUSCLE THICKNESS MEASURED BY POINT-OF-CARE ULTRASOUND AND HOSPITAL LENGTH OF STAY
Bibliographic record
Abstract
Abstract Background Accurate prediction of hospital length of stay (LOS) and readmission rates could help improve effective healthcare resource allocation. Recent evidence suggests point-of-care ultrasound for muscle assessment, specifically muscle thickness, as a promising tool in this regard. This study explores the hypothesis that ultrasound measurements of quadriceps muscle thickness (MT) and echointensity (EI) can serve as predictors for these crucial patient outcomes. Methods The study measured quadriceps MT and EI using point-of-care ultrasound for patients in a supine position in the emergency department of Vancouver General Hospital. Predictor variables included age, sex, muscle thickness, and echo intensity. Outcome variables were hospital LOS, readmission rate, and discharge destination. Follow-up was conducted after one month to assess hospital readmissions and mortality. Results A total of 120 participants were included (average age 76.9 ± 7.5, with 64 women and 56 men). Mean LOS was 27.4 ± 31.4 days, and mean MT was 20 ± 6 mm. Sex-based differences in MT were statistically significant (P = 0.032). MT correlated significantly with LOS (Standardized β = -0.152 ± 0.016, R² = 0.290, P = 0.001). In the multivariate regression model, MT remained a significant predictor (Standardized β = -0.152 ± 0.563, P = 0.008). Conclusion Muscle thickness is a significant predictor of hospital stay duration. The findings of this study indicate the potential of point-of-care ultrasound in measuring skeletal muscle as an effective predictor of discharge outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".