Quadriceps muscle thickness as measured by point-of-care ultrasound is associated with hospital length of stay among hospitalised older patients
Bibliographic record
Abstract
BACKGROUND: Predicting hospital length of stay (LOS) can potentially improve healthcare resource allocation. Recent studies suggest that point-of-care ultrasound (POCUS), specifically measurements of muscle thickness (MT), may be valuable in assessing patient outcomes, including LOS. This study investigates the hypothesis that quadriceps MT and echo intensity (EI) can predict patient outcomes, particularly LOS. METHODS: Quadriceps MT and EI were measured using POCUS in patients admitted to a hospital's acute medical unit. Predictor variables included age, sex, MT, EI and the Charlson Comorbidity Index (CCI). The outcome variable was hospital LOS. RESULTS: One hundred twenty participants were included (average age 76 ± 7, with 64 women and 56 men). The mean LOS was 27 ± 31 days, and the mean MT was 20 ± 6 mm. Sex-based differences in MT were statistically significant (P = .032). Patients with prolonged LOS over 30 days had lower MT (mean 17 mm vs. 21 mm, P < .0001). One unit increase in MT was significantly associated with ~1.5 fewer days of hospital LOS, and one CCI score increase was associated with almost three more days of hospital LOS. Having low MT significantly increased the odds of staying in the hospital longer than 30 days by more than three times in all models. CONCLUSION: Muscle thickness is a strong predictor of hospital LOS, highlighting the potential of POCUS for assessing patient outcomes.
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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 | 0.000 |
| 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".