Burn-induced hypermetabolism and skeletal muscle dysfunction
Bibliographic record
Abstract
Critical illnesses, including sepsis, cancer cachexia, and burn injury, invoke a milieu of systemic metabolic and inflammatory derangements that ultimately results in increased energy expenditure leading to fat and lean mass catabolism. Burn injuries present a unique clinical challenge given the magnitude and duration of the hypermetabolic response compared with other forms of critical illness, which drastically increase the risk of morbidity and mortality. Skeletal muscle metabolism is particularly altered as a consequence of burn-induced hypermetabolism, as it primarily provides a main source of fuel in support of wound healing. Interestingly, muscle catabolism is sustained long after the wound has healed, indicating that additional mechanisms beyond wound healing are involved. In this review, we discuss the distinctive pathophysiological response to burn injury with a focus on skeletal muscle function and metabolism. We first examine the diverse consequences on skeletal muscle dysfunction between thermal, electrical, and chemical burns. We then provide a comprehensive overview of the known mechanisms underlying skeletal muscle dysfunction that may be attributed to hypermetabolism. Finally, we review the most promising current treatment options to mitigate muscle catabolism, and by extension improve morbidity and mortality, and end with future directions that have the potential to significantly improve patient care.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".