Preoperative immunonutrition and postoperative outcomes in patients with cancer undergoing major abdominal surgery: Retrospective cohort study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Surgical resection is a first-line treatment for patients with cancer, but preoperative malnutrition is a risk factor for postoperative complications. This study aimed to evaluate the association between preoperative administration of an immunonutrition regimen and postoperative clinical outcomes in patients with cancer undergoing major abdominal surgery. METHODS: The Surgical Prehabilitation Multimodal Oncology (SUPREMO) retrospective cohort study, conducted from January 2021 to December 2023, included patients with cancer undergoing major abdominal surgery. Patients were categorized based on whether they received a complete immunonutrition regimen or an incomplete or no regimen. Demographic and clinical data were extracted from electronic health records for descriptive analysis. Logistic regression was used to assess the impact of immunonutrition on the risk of infectious complications, with clinical and demographic variables as explanatory factors. RESULTS: A total of 620 patients were included, with 49 % receiving a complete preoperative immunonutrition regimen. Bivariate analysis indicated that complete regimen administration was associated with lower intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and vasopressor support requirements (p = 0.005, p = 0.019, and p = 0.032, respectively). The logistic regression model showed a significant reduction in in-hospital infectious complications (odds ratio 0.54, 95 % confidence interval 0.31-0.98; p = 0.044). CONCLUSION: Administering a complete preoperative immunonutrition regimen may be associated with reduced infectious complications, ICU and IMV requirements, and vasopressor support use.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".