Malnutrition in Burns: A Prospective, Single-Center Study
Bibliographic record
Abstract
The hypermetabolic response from a burn injury is the highest of the critically ill patient population. When coupled with the hypermetabolic response, preexisting malnutrition may increase the hospital resources used. The goal of this study was to evaluate the rate of malnutrition in burn patients and the associated hospital resource utilization. We collected prospective data on burn patients 18 years or older with a burn at least 10% TBSA admitted to a regional burn center. Demographics, %TBSA, comorbidities, length of stay (LOS), and standardized LOS (LOS/%TBSA) were evaluated on 49 patients. A multivariable regression model was constructed. Nutrition assessment was completed within 24 to 48 hours of admission including an SGA (Subjective Global Assessment) classification. SGA A (well-nourished) was compared to SGA B and C (malnourished). Fourteen patients (28.6%) in this study were malnourished. Malnourished patients were not statistically different with respect to median age (50 vs 39; P = .08) and body mass index (22.9 vs 26.5; P = .08) compared to the well-nourished group. However, malnourished patients had significantly longer median LOS (21.0 vs 11.0 days, P = .01) and LOS/%TBSA (1.69 vs 0.83, P = .001) than the well-nourished group. Being malnourished was a significant independent predictor of above-median LOS/%TBSA (P = .027) with an odds ratio of 5.61 (95% CI 1.215-25.890). The rate of malnutrition is important given the high metabolic demands of these patients. Malnutrition increased the resource requirements via higher standardized LOS. This underscores the importance of completing SGA on admission to identify malnutrition early on to optimize nutrition intervention during the patients' hospital stay.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".