The location and degree of residual disease determines recurrence patterns and survival in patients with esophageal adenocarcinoma after trimodal therapy
Bibliographic record
Abstract
Known predictors of recurrence and survival include the total number of nodes and positive nodes resected, tumor stage, histology, and tumor differentiation. Patients with a complete response have the best survival, but residual tumor in the esophagus, nodes, or both may influence survival. This study assesses the risk of recurrence and survival of esophageal adenocarcinoma after trimodal therapy based upon the location residual disease in the resected specimen. Multicenter, retrospective study of patients with esophageal adenocarcinoma undergoing induction chemoradiation and transthoracic esophagectomy from 2010 to 2017. Overall survival (OS) and recurrence were compared based on the residual disease location using Kaplan-Meier analysis. Clinical factors associated with OS and time to recurrence were also assessed. There were 504 patients with a median follow-up of 63.4 months 95% confidence interval (CI):60.8-70.5 and an estimated overall 5-year survival of 55.8%. When subdivided by residual disease location, the estimated 5-year survival in patients with complete pathologic response and residual disease of only the esophagus was 68.3% and 65.0% hazard ratio (HR) = 1.05; P = 0.81. With increasing nodal positivity there was decreasing survival, 45.8% (N1) and 20.1% (N2). N3 patients did not survive past 36 months. Multivariable analysis demonstrates that any residual disease in the lymph nodes (HR = 3.14), taxane and fluoropyrimidine chemotherapy (HR = 3.34), neoadjuvant radiation dose <50 Gy (HR = 2.35), and fluoropyrimidine chemotherapy (HR = 1.88) were predictive of worse OS. Overall survival and recurrence are influenced by the location of residual disease. Residual disease in the esophagus and pathologic complete response behaves similarly. Survival is reduced as nodal counts increase.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".