How Much and What Type of Protein Should a Critically Ill Patient Receive?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Protein loss, manifested as loss of muscle mass, is observed universally in all critically ill patients. Depletion of muscle mass is associated with impaired function and poor outcomes. In extreme cases, protein malnutrition is manifested by respiratory failure, lack of wound healing, and immune dysfunction. Protecting muscle loss focused initially on meeting energy requirements. The assumption was that protein was being used (through oxidation) as an energy source. In healthy individuals, small amounts of glucose (approximately 400 calories) protect muscle loss and decrease amino acid oxidation (protein-sparing effect of glucose). Despite expectations of the benefits, the high provision of energy (above basal energy requirements) through the delivery of nonprotein calories has failed to demonstrate a clear benefit at curtailing protein loss. The protein-sparing effect of glucose is not clearly observed during illness. Increasing protein delivery beyond the normal nutrition requirements (0.8 g/k/d) has been investigated as an alternative solution. Over a dozen observational studies in critically ill patients suggest that higher protein delivery is beneficial at protecting muscle mass and associated with improved outcomes (decrease in mortality). Not surprisingly, new Society of Critical Care Medicine/American Society for Parenteral and Enteral Nutrition guidelines and expert recommendations suggest higher protein delivery (>1.2 g/kg/d) for critically ill patients. This article provides an introduction to the concepts that delineate the basic principles of modern medical nutrition therapy as it relates to the goal of achieving an optimal management of protein metabolism during critical care illness, highlighting successes achieved so far but also placing significant challenges limiting our success in perspective.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it