Current Literature: Immunonutrition in the Critically Ill: A Systematic Review of Clinical Outcome
Bibliographic record
Abstract
Objective: To perform a meta‐analysis addressing whether enteral nutrition with immune‐enhancing feeds benefits critically ill patients after trauma, sepsis, or major surgery. Data Sources: Studies were identified by MEDLINE search (1967 to January 1998) for original articles in English using the search terms “human,” “enteral nutrition,” “arginine,” “nucleotides,” “omega‐3 fatty acids,” “immunonutrition,” “IMPACT,” and “Immun‐Aid.” Additionally, the authors of the studies and the manufacturers of the feedings were contacted for addition Information. Access to original databases was obtained for the three largest studies. Study Selection: Fifteen randomized controlled trials comparing patients receiving standard enteral nutrition with patients receiving a commercially available immune‐enhancing feed with arginine with or without glutamine, nucleotides, and omega‐3 fatty acids ? were identified by two independent reviewers (Dr. Beale and Dr. Bryg). Data Extraction: Descriptive and outcome data were extracted independently from the papers by the same two reviewers, one of whom (Dr. Bryg) analyzed the original databases. Three studies were excluded from analysis, leaving 12 studies containing 1557 subjects, 1482 of whom were analyzed. Main outcome measures were mortality, infection, ventilator days, intensive care unit stay, hospital stay, diarrhea days, calorie intake, and nitrogen intake. The meta analysis was performed on an intent‐to‐treat basis. Data Synthesis: There was no effect of immunonutrition on mortality (relative risk = 1.05, confidence interval [CI] = 0.78, 1.41; p = .76). There were significant reductions in infection rate (relative risk = 0.67, CI = 0.50,0.89; p = .006), ventilator days (2.6 days, CI = 0.1, 5.1; p = .04), and hospital length of stay (2.9 days, CI = 1.4, 4.4; p = .0002) in the immunonutrition group. Conclusions: The benefits of enteral immunonutrition were most pronounced in surgical patients, although they were present in all groups. The reduction in hospital length of stay and infections has resource implications. (Crit Care Med 27:2799–805, 1999)
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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 | 0.031 | 0.094 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.024 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".