Does Perioperative Immunonutrition Reduce Postoperative Complications in Patients with Gastrointestinal Cancer Undergoing Operations?
Bibliographic record
Abstract
Perioperative immune modulation using specialized enteral diets containing specific immunonutrients may improve postoperative outcomes in critically ill patients compared with standard formulas. A study from Italy involving 305 patents with histologically confirmed cancer of the gastrointestinal tract undergoing major elective surgery and preoperative weight loss < 10% demonstrated that a specialized preoperative oral formula enriched with arginine, omega-3 fatty acids, and RNA for 5 days before surgery with no nutritional support postoperatively (preoperative group) was as effective as pre- and postoperative administration of the same enriched formula (perioperative group) in decreasing the incidence of postoperative infections and length of hospital stay. Both pre- and perioperative immunonutritional strategies were superior to the conventional approach (no artificial nutrition perioperatively).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".