Anabolic and anticatabolic agents in critical care
Bibliographic record
Abstract
PURPOSE OF REVIEW: A complex network of hormones and other effectors characterize the hypermetabolic response in critical illness; these mediators work together to induce numerous pathophysiologic alterations. Increased incidence of infection, multiorgan failure, long-term debilitation, delays in rehabilitation, and death result from an inability to meet the prohibitively elevated protein and energy requirements, which occur during illness and can persist for several years. Pharmacologic interventions have been successfully utilized to attenuate particular aspects of the hypermetabolic response; these modalities are a component of managing critically ill patients - including those patients with severe burns. Here, we review recent advances in pharmacologically attenuating the hypermetabolic and catabolic responses. RECENT FINDINGS: Propranolol, a nonspecific β-adrenergic receptor antagonist, is one of the most widely used anticatabolic therapies. Oxandrolone, testosterone, and intensive insulin therapy represent anabolic pharmacological strategies. Promising therapies, such as metformin, glucagon-like peptide 1, peroxisome proliferator-activated receptor agonists, are currently being investigated. SUMMARY: Profound metabolic derangements occur in critically ill patients; this hypermetabolic response is a major contributor to adverse outcomes. Despite the pharmacological therapies currently available to counteract this devastating cascade, future studies are warranted to explore new multimodality agents that will counteract these effects while maintaining glycemic control and preventing unfavorable complications.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".