Muscular Ultrasound, Syndecan-1 and Procalcitonin Serum Levels to Assess Intensive Care Unit-Acquired Weakness
Bibliographic record
Abstract
BACKGROUND: Intensive care unit-acquired weakness (ICU-AW) is associated with poorer outcome of critically ill patients. Microcirculatory changes and altered vascular permeability of skeletal muscles might contribute to the pathogenesis of ICU-AW. Muscular ultrasound (MUS) displays increased muscle echogenicity, although its pathogenesis is uncertain. OBJECTIVE: We investigated the combined measurement of serum and ultrasound markers to assess ICU-AW and clinical patient outcome. METHODS: Fifteen patients and five healthy controls were longitudinally assessed for signs of ICU-AW at study days 3 and 10 using a muscle strength sum score. The definition of ICU-AW was based on decreased muscle strength assessed by the muscular research council-sum score. Ultrasound echogenicity of extremity muscles was assessed using a standardized protocol. Serum markers of inflammation and endothelial damage were measured. The 3-month outcome was assessed on the modified Rankin scale. RESULTS: ICU-AW was present in eight patients, and seven patients and the control subjects did not develop ICU-AW. The global muscle echogenicity score (GME) differed significantly between controls and patients (mean GME, 1.1 ± 0.06 vs. 2.3 ± 0.41; p = 0.001). Mean GME values significantly decreased in patients without ICU-AW from assessment 1 (2.30 ± 0.48) to assessment 2 (2.06 ± 0.45; p = 0.027), which was not observed in patients with ICU-AW. Serum levels of syndecan-1 at day 3 significantly correlated with higher GME values at day 10 (r = 0.63, p = 0.012). Furthermore, the patients' GME significantly correlated with mRS at day 100 (r = 0.67, p = 0.013). CONCLUSION: The combined use of muscular ultrasound and inflammatory biomarkers might be helpful to diagnose ICU-AW and to predict long-term outcome in critical illness.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".