Nutrition and Anabolic Pharmacotherapies in the Care of Burn Patients
Bibliographic record
Abstract
Thermal injury is a devastating injury that results in a number of pathological alterations in almost every system in the body. Hypermetabolism, muscle wasting, depressed immunity, and impaired wound healing are all clinical features of burns. Failure to address each of these specific pathological alterations can lead to increased mortality. Nutrition supplementation has been recommended as a therapeutic tool to help attenuate the hypermetabolism and devastating catabolism evident following burn. Despite the wide consensus on the need of nutrition supplementation in burn patients, controversy exists with regard to the type and amount of nutrition recommended. Nutrition alone is also not enough in these patients to halt and reverse some of the damage done by the catabolic pathways activated following severe burn injury. This has led to the use of anabolic pharmacologic agents in conjunction with nutrition to help improve patient outcome following burn injury. In this review, we examine the relevant literature on nutrition after burn injury and its contribution to the attenuation of the postburn hypermetabolic response, impaired wound healing, and suppressed immunological responses. We also review the commonly used anabolic agents clinically in the care of burn patients. Finally, we provide nutrition and pharmacological recommendations gained from prospective trials, retrospective analyses, and expert opinions based on our practice at the Ross Tilley Burn Center in Toronto, Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".