Computed Tomography Measurements of Sarcopenia Predict Length of Stay in Older Burn Patients
Bibliographic record
Abstract
Sarcopenia and frailty are associated with aging. In older burn patients, frailty has been associated with mortality and discharge disposition, but sarcopenia has not been examined. This study aims to investigate the relationship between frailty and computed tomography (CT)-derived sarcopenia with length of stay and mortality in older burn patients. Burn patients ≥60 years old admitted between 2008 and 2017 who had chest or abdomen CT scans within 1 week of admission were evaluated. Frailty was assessed using the Canadian Study of Health and Aging Clinical Frailty Scale (CFS). Sarcopenia was assessed on CT exams by measuring skeletal muscle index (SMI) of paraspinal muscles at T12 and all skeletal muscles at L3. The relationship between frailty scores and SMI with length of stay (LOS) and mortality was determined using logistic regression. Eighty-three patients (59 men; mean age 70.2 ± 8.5 years) had chest (n = 50) or abdomen (n = 60) CT scans. Mean TBSA = 14.3 ± 14.0%, LOS = 25.8 ± 21.3 days, CFS = 4.36 ± 0.99. Sixteen patients (19.3%) died while in the hospital. CT-derived measurement of SMI at T12 was significantly associated with LOS (P < .05), but not with mortality (P = .561). CT-derived metrics at L3 were not significantly associated with outcomes. CFS was not associated with LOS (P = .836) or mortality (P = .554). In older burn patients, low SMI of the paraspinal muscles at T12 was associated with longer LOS.
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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 | 0.000 | 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".