A Systematic Review of Strategies for Preoperative Nutrition for Esophageal Cancer Patients Undergoing Neoadjuvant Therapy: A Comparison of Nasoenteric Nutrition, Surgical Tubes, and Stent Placement
Bibliographic record
Abstract
Introduction: Esophageal cancer often presents at a late stage. Along with tumor metabolic burden, dysphagia can lead to malnutrition and worsen outcomes in esophageal cancer. Various nutrition modalities have been trialed for patients undergoing neoadjuvant therapy prior to esophagectomy but there is a lack of consensus as to which strategy is optimal. Methods: A systematic review of Ovid MEDLINE, Ovid Embase, Scopus, Web of Science Core Collection, and Cochrane Library was performed. Studies were included that examined esophageal cancer patients undergoing neoadjuvant therapy, assessing preoperative nutritional strategies including nasoenteric feeds, percutaneous (PEG) or surgical gastrostomy (G-tube) or jejunostomy tubes (J-tube), and esophageal stents. Risk of bias was assessed using the MINORS tool. Results: Of 3393 retrieved records, 23 were included. Nutritional strategies studied included stents (n = 16), J-tube (n = 8), G-tube (n = 3), nasogastric tube (NG, n = 2), and PEG (n = 2). Most feeding strategies showed mixed results regarding improvement, stability or decrease in weight or albumin during neoadjuvant therapy. Complications appeared most severe for endoscopic or surgical tubes (obstruction, migration) and stents (perforation, migration, intolerance), which occasionally required operation. Conclusion: There is significant heterogeneity in the literature on whether preoperative feeding modalities are associated with improved nutritional outcomes in this population. At the same time, endoscopic or surgical feeding tubes and esophageal stents are clearly associated with higher risk complications. Future studies standardizing study designs, done in a prospective comparative fashion may be beneficial, along with identification of patients who would benefit most.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".