43 Computed Tomography (CT) Measurements of Sarcopenia Predict Length of Stay but not Mortality in Elderly Burn Population
Bibliographic record
Abstract
Frailty and sarcopenia (loss of skeletal muscle mass) are two factors associated with aging. Previous studies indicate frailty scores are related to mortality, but sarcopenia has not been examined in elderly burn patients. Our goal was to correlate frailty and sarcopenia with mortality and length of stay (LOS) in burn patients. All burn patients≥60 years old admitted between 2008–2017 who had CT scans of the head, chest or abdomen/pelvis within a week of admission were evaluated. Data included: age, sex, burn size (% TBSA), inhalation injury, LOS, and mortality. Frailty scores were evaluated on admission by two independent practitioners using the Canadian Study of Health and Aging Clinical Frailty Scale (CSHA CFS). On CT, the masseter muscles, paraspinal muscles at T12, and all skeletal muscles at L3 were analyzed for skeletal muscle mass index (SMI) and Hounsfield units (HU). Values are presented as means ± standard deviation. 143 patients (47 females and 96 males; 21.9% of all patients over 60 years old) underwent head (n=69), chest (n=50) or abdomen/pelvis (n=60) CT scans. Mean age was 70.4±8.7 years with a mean TBSA of 15.0±14.2% and a mean LOS of 25.2±20.6 days. Twenty-seven patients (18.9%) had inhalation injury; 28 (19.5%) died of their injuries while in the hospital. Logistic regression showed that the log odds of mortality significantly increased with TBSA but not age. The mean CSHA CFS was 4.34±1.0 and was not a predictor of mortality (p = 0.15) or LOS (p = 0.452). None of the CT metrics (at the level of the masseter, T12 or L3) were predictors of mortality while in the hospital. At L3 none of the CT metrics were significant predictors of LOS. However, T12 and masseter CT measurements of SMI were predictors of LOS (p<0.05). Patients in the “normal” range for SMI had significantly shorter LOS than those with sarcopenia. None of the HU metrics were significant. Previously, CSHA CFS has been predictive of mortality in an elderly burn population; however in the sub-group of patients who underwent CT scans this was not the case. Measurements of low SMI of the masseter muscle on head CT and of the paraspinal muscles at T12 were predictive of longer LOS. Only a small percentage of elderly burn patients received a CT scan. Further study is needed to determine the ability of frailty and sarcopenia to predict outcomes in an elderly burn population. Sarcopenia and frailty should be considered as predictors of poor outcomes. Use of masseter muscle measurements for sarcopenia is a novel approach that needs to be further studied and additional modalities for measuring sarcopenia beyond CT scan should be examined.
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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 | 0.001 |
| 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) | 0.002 | 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".