Clinical Evidence for Pharmaconutrition in Major Elective Surgery
Bibliographic record
Abstract
In recent years, standard nutrition preparations have been modified by adding specific nutrients, such as arginine, ω-3 fatty acids, glutamine, and others, which have been shown to upregulate host immune response, modulate inflammatory response, and improve protein synthesis after surgery. Most randomized trials and several meta-analyses have shown that perioperative administration of enteral arginine, ω-3 fatty acids, and nucleotides (immunonutrition) reduced infection rate and length of hospital stay in patients with upper and lower gastrointestinal (GI) cancer. The most pronounced benefits of immunonutrition were found in subgroups of high-risk and malnourished patients. Promising but not conclusive results have been found in non-GI surgery, especially in head and neck surgery and in cardiac surgery, but larger trials are required before recommending immunonutrition as a routine practice. Conflicting results on the real benefit of parenteral glutamine supplementation in patients undergoing elective major surgery have been published. In conclusion, enteral diets supplemented with specific nutrients significantly improved short-term outcome in patients with cancer undergoing elective GI surgery. Future research should investigate a molecular signaling pathway and identify specific mechanisms of action of immune-enhancing substrates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".