Bibliographic record
Abstract
PURPOSE OF REVIEW: In the past year, there have been a few large-scale trials of nutrition support in the critical care setting that have produced negative results and have challenged certain assumptions. The purpose of this study is to review those current trials and illustrate key methodological points that should help with the interpretation of these trials, and inform the design of critical care nutrition trials of artificial nutrition in the future. RECENT FINDINGS: Many recent and historical randomized trials of nutrition support in the ICU setting fail to consider which patients may benefit the most from artificial nutrition support (nutrition risk assessment) and longer-term outcomes such as return to physical function and health-related quality of life. SUMMARY: Future trials of nutrition support in the ICU, such as the TOP UP study, should include only 'high-risk' patients and should evaluate a broader range of outcomes than traditional ICU outcomes (28-day mortality, ventilator-free days, organ failure-free days, etc.). In the meantime, efforts to improve delivery of energy and protein to critically ill patients, such as with the enhanced protein-energy provision via the enteral route feeding protocol, are warranted.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".