Prospective Study on Energy Expenditure in Patients With Severe Burns
Bibliographic record
Abstract
BACKGROUND: Nutrition therapy is recognized as one of the most significant treatment aspects for burn patients. However, data were limited regarding the actual nutrition practices in patients with severe burn injury. This study aims to explore the measured energy expenditure (MEE) changes in severe burn patients and to evaluate the precision of commonly used predictive formulas for estimating predictive energy expenditure (PEE) in burn patients. METHODS: A prospective multicenter trial was conducted in the intensive care units in the hospitals enrolling the severely burned patients. Data on MEE and PEE were collected and analyzed. RESULTS: Forty-three patients were enrolled from 3 hospitals. All the patients had severe burns. MEE was measured by metabolic cart, and the MEE on the seventh day after severe burns was as high as 65 kcal/kg, which was 267% of the basal metabolic rate. The presence of hypermetabolism was sustained throughout the 21-day afterburn and decreased gradually to 34 kcal/kg thereafter until 4 weeks after injury. Wound percentage after skin-grafting therapy, time course of burn injury, the existence of severe sepsis, and blood infection were significantly associated with higher MEE. Compared with PEE and MEE, Toronto formula could estimate patients' energy requirements with more accuracy; Curreri and Pennisi formula both significantly overestimated the patient's energy expenditure, whereas underestimation occurred with the Harris-Benedict formula. CONCLUSIONS: Severe burn patients were hypermetabolic at the early stage and sustained this status over a long time. The Toronto formula was the unbiased method to predict energy expenditure.
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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 | 0.001 |
| 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 | 0.001 |
| 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".