The Impact of Peri-Operative Nutritional Status on Survival in Gastroesophageal Adenocarcinoma
Bibliographic record
Abstract
In patients with gastric, gastroesophageal junction or esophageal adenocarcinoma (GOC), peri-operative multimodal therapies have improved survival; however, prognosis remains underwhelming. Pre-operative nutritional decline and weight are linked with poorer patient outcomes. This study retrospectively analyzed the impact of peri-operative nutritional status (as assessed by patient-generated subjective global assessment, PG-SGA), and weight loss on the survival of patients undergoing curative surgery for GOC (2013 to 2022). Of the 148 patients who underwent surgery, PG-SGA and weight data were available for 107 (72%) and 121 (82%), respectively. At presentation, 44% (n = 47) of patients were well nourished, dropping to 17% (n = 18) post-operatively. Lower post-operative nutritional status correlated to worse overall survival (OS) (p < 0.001). Patients who stayed well nourished or improved their nutritional status had better survival outcomes (HR: 2.7; 95%CI: 1.2–6.1; p = 0.01). Significant weight loss (>10%) was ubiquitously observed in 54% (n = 64) of patients, and this group had shorter OS (HR: 2.2; 95%CI: 1.2–4.1; p = 0.009). In conclusion, both nutritional decline and weight loss negatively impacted survival. Maintenance of nutritional status over the peri-operative period resulted in better outcomes. This study highlights the need for improved nutritional support during curative treatment in GOC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".