Will We Ever Agree on Protein Requirements in the Intensive Care Unit?
Bibliographic record
Abstract
The precise value of the normal adult protein requirement has long been debated. For many reasons-one of them being the difficulty of carrying out long-term nutrition experiments in free-living people-uncertainty is likely to persist indefinitely. By contrast, the controlled environment of the intensive care unit and relatively short trajectory of many critical illnesses make it feasible to use hard clinical outcome trials to determine protein requirements for critically ill patients in well-defined clinical situations. This article suggests how the physiological principles that underlie our understanding of normal protein requirements can be incorporated into the design of such clinical trials. The main focus is on 3 principles: (1) the rate of body nitrogen loss roughly predicts an individual's minimum protein requirement and is thus essential to measure to identify individual patients and clinical situations in which the minimum protein requirement is importantly increased, (2) existing muscle mass sets an upper limit on the rate at which amino acids can be mobilized from muscle for transfer to central proteins and sites of injury and is thus important to monitor to identify patients who are at greatest risk of protein deficiency-related adverse outcomes, and (3) negative energy balance increases the dietary protein requirement, so calorie-deprived patients-whether obese or not-should be enrolled in hard clinical outcome trials that compare the current practice of "permissive underfeeding" (underprovision of all nutrients, including protein) with hypocaloric nutrition supplemented by a suitably generous amount of protein.
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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 | 0.048 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".