How Much and What Type of Protein Should a Critically Ill Patient Receive?
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".