Skeletal Muscle Quality Evaluation for Prognostic Stratification in the Emergency Department of Patients ≥ 65 Years with Major Trauma
Bibliographic record
Abstract
Background: In patients over 65 years who experience severe trauma the underlying health status has a significant impact on overall mortality. This study aims to assess if CT evaluation of skeletal muscle quality could be a risk stratification tool in the ED for these patients. Methods: Retrospective observational study between January 2018 and September 2021, including consecutive patients ≥ 65 years admitted to the ED for a major trauma (defined as Injury Severity Score > 15). Muscle quality analysis was made by specific software (Slice-O-Matic v5.0, Tomovision®, Montreal, QC, Canada) on a CT-Scan slice at the level of the third lumbar vertebra. Results: 263 patients were included (72.2% males, median age 76 [71-82]), and 88 (33.5%) deceased. The deceased patients had a significantly lower skeletal muscle area density (SMAd) compared to survivors. The multivariate Cox regression analysis confirmed that SMAd < 38 at the ED admission was an independent risk for death (adjusted HR 1.68 [1.1 – 2.7]). The analysis also revealed that, among the survivors after the first week of hospitalization, the patients with low SMAd had an increased risk of death (adjusted HR 3.12 [1.2 – 7.9]). Conclusions: The skeletal muscle density evaluated by a CT scan at ED admission could be a valuable risk stratification tool for patients ≥ 65 years with major trauma. In patients with SMAd <38 HU the in-hospital mortality risk could be particularly increased after the first week of hospitalization.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".