Skeletal Muscle Quality Evaluation for Prognostic Stratification in the Emergency Department of Patients ≥65 Years with Major Trauma
Bibliographic record
Abstract
Background: In patients aged 65 years and older who experience severe trauma, their underlying health status significantly influences overall mortality. This study aimed to determine whether computed tomography (CT) evaluation of skeletal muscle quality could serve as an effective risk stratification tool in the emergency department (ED) for this population. Methods: Retrospective observational study conducted between January 2018 and September 2021, including consecutive patients ≥65 years admitted to the ED for a major trauma (defined as having an 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 (L3). Results: A total of 263 patients were included (72.2% males, median age 76 (71–82)), of whom 88 (33.5%) died during hospitalization. The deceased patients had a significantly lower skeletal muscle area density (SMAd) compared with 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: Skeletal muscle density assessed by a CT scan at ED admission may represent a valuable prognostic marker for risk stratification 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 | 0.001 | 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".