Nutrition management by a multidisciplinary team for prevention of nutritional deficits and morbidity following esophagectomy
Bibliographic record
Abstract
This study evaluated the effects of perioperative nutrition management by a multidisciplinary team on nutrition and postoperative complications of patients with esophageal cancer. A total of 239 patients with esophageal cancer who underwent esophagectomy and gastric conduit reconstruction for esophageal or esophagogastric junction cancer between February 2019 and February 2020 were included in the study. They were divided into the experimental group (120 patients) and the control group (119 patients) using the random number table method. Control group patients received routine diet management and experimental group patients received perioperative nutrition management by a multidisciplinary team. The differences of nutriture and postoperative complications between the two groups were compared. At 3 and 7 days after surgery, the experimental group patients had higher total protein and albumin levels (P<0.05), shorter postoperative anal exhaust time (P<0.05), lower incidence of postoperative gastrointestinal adverse reactions, pneumonia, anastomotic fistula, hypoproteinemia (P<0.05), and lower hospitalization costs (P<0.05) than the control group. Nutrition management by a multidisciplinary team effectively improved the nutriture of patients, promoted the rapid recovery of postoperative gastrointestinal function, reduced postoperative complications, and reduced hospitalization costs.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".