Feeding the Critically Ill Patient
Bibliographic record
Abstract
OBJECTIVE: Critically ill patients are usually unable to maintain adequate volitional intake to meet their metabolic demands. As such, provision of nutrition is part of the medical care of these patients. This review provides detail and interpretation of current data on specialized nutrition therapy in critically ill patients, with focus on recently published studies. DATA SOURCES: The authors used literature searches, personal contact with critical care nutrition experts, and knowledge of unpublished data for this review. STUDY SELECTION: Published and unpublished nutrition studies, consisting of observational and randomized controlled trials, are reviewed. DATA EXTRACTION: The authors used consensus to summarize the evidence behind specialized nutrition. DATA SYNTHESIS: In addition, the authors provide recommendations for nutritional care of the critically ill patient. CONCLUSIONS: Current evidence suggests that enteral nutrition, started as soon as possible after acute resuscitative efforts, may serve therapeutic roles beyond providing calories and protein. Although many new studies have further advanced our knowledge in this area, the appropriate level of standardization has not yet been achieved for nutrition therapy, as it has in other areas of critical care. Protocolized nutrition therapy should be modified for each institution based on available expertise, local barriers, and existing culture in the ICU to optimize evidence-based nutrition care for each critically ill patient.
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 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 | 0.000 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".