Summary Points and Consensus Recommendations From the International Protein Summit
Bibliographic record
Abstract
The International Protein Summit in 2016 brought experts in clinical nutrition and protein metabolism together from around the globe to determine the impact of high-dose protein administration on clinical outcomes and address barriers to its delivery in the critically ill patient. It has been suggested that high doses of protein in the range of 1.2-2.5 g/kg/d may be required in the setting of the intensive care unit (ICU) to optimize nutrition therapy and reduce mortality. While incapable of blunting the catabolic response, protein doses in this range may be needed to best stimulate new protein synthesis and preserve muscle mass. Quality of protein (determined by source, content and ratio of amino acids, and digestibility) affects nutrient sensing pathways such as the mammalian target of rapamycin. Achieving protein goals the first week following admission to the ICU should take precedence over meeting energy goals. High-protein hypocaloric (providing 80%-90% of caloric requirements) feeding may evolve as the best strategy during the initial phase of critical illness to avoid overfeeding, improve insulin sensitivity, and maintain body protein homeostasis, especially in the patient at high nutrition risk. This article provides a set of recommendations based on assessment of the current literature to guide healthcare professionals in clinical practice at this time, as well as a list of potential topics to guide investigators for purposes of research in the future.
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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 | 0.077 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.022 | 0.018 |
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".