Esophagectomy or Total Gastrectomy for Siewert 2 Gastroesophageal Junction (GEJ) Adenocarcinoma? A Registry-Based Analysis
Bibliographic record
Abstract
BACKGROUNDS: Due to a lack of randomized and large studies, the optimal surgical approach for Siewert 2 gastroesophageal junctional (GEJ) adenocarcinoma remains unknown. This population-based cohort study aimed to compare survival between esophagectomy and total gastrectomy for the treatment of Siewert 2 GEJ adenocarcinoma. METHODS: Data from the National Cancer Database (NCDB) from 2010 to 2016 was used to identify patients with non-metastatic Siewert 2 GEJ adenocarcinoma who received either esophagectomy (n = 999) or total gastrectomy (n = 8595). Propensity score-matching (PSM) and multivariable analyses were used to account for treatment selection bias. RESULTS: Comparison of the unmatched cohort's baseline demographics showed that the patients who received esophagectomy were younger, had a lower burden of medical comorbidities, and had fewer clinical positive lymph nodes. The patients in the unmatched cohort who received gastrectomy had a significantly shorter overall survival than those who received esophagectomy (median, 47 vs. 68 months [p < 0.001]; 5-year survival, 45 % vs. 53 %). After matching, gastrectomy was associated with significantly reduced survival compared with esophagectomy (median, 51 vs. 68 months [p < 0.001]; 5-year survival, 47 % vs. 53 %), which remained in the adjusted analyses (hazard ratio [HR], 1.22; 95 % confidence interval [CI], 1.09-1.35; p < 0.001). CONCLUSIONS: In this large-scale population study with propensity-matching to adjust for confounders, esophagectomy was prognostically superior to gastrectomy for the treatment of Siewert 2 GEJ adenocarcinoma despite comparable lymph node harvest, length of stay, and 90-day mortality. Adequately powered randomized controlled trials with robust surgical quality assurance are the next step in evaluating the prognostic outcomes of these surgical strategies for GEJ cancer.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".