Role of Adjuvant Therapy in Esophageal Cancer Patients After Neoadjuvant Therapy and Esophagectomy
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to analyze esophageal cancer patients who previously underwent neoadjuvant therapy followed by a curative resection to determine whether additional adjuvant therapy is associated with improved survival outcomes. SUMMARY BACKGROUND DATA: Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal cancer, whereas adjuvant therapy is typically employed for patients with residual disease. However, the role of adjuvant therapy after a curative resection is still uncertain. METHODS: MEDLINE, EMBASE, and CENTRAL databases were searched for studies comparing patients with esophageal cancer who underwent neoadjuvant therapy and curative resection with and without adjuvant therapy. Primary outcome was overall survival (OS), and random effects meta-analysis was conducted where appropriate. Grading of recommendations, assessment, development, and evaluation was used to assess the certainty of evidence. RESULTS: Ten studies involving 6462 patients were included. When compared to patients who received neoadjuvant therapy and esophagectomy alone, adjuvant therapy groups experienced a significant decrease in mortality by 48% at 1 year (Risk Ratio (RR) 0.52, 95% confidence interval [CI] 0.41-0.65, P < 0.001, moderate certainty). This reduction in mortality was carried through to 5-year follow-up (RR 0.91, 95% CI 0.86-0.96, P < 0.001, moderate certainty). The difference between the adjuvant therapy and the control group was uncertain regarding the secondary outcomes. CONCLUSION: Adjuvant therapy after neoadjuvant treatment and esophagectomy with negative resection margins provide an improved OS at 1 and 5 years with moderate to high certainty of evidence, but the benefit for disease-free survival and locoregional/distal recurrence remain uncertain due to limited reporting of these outcomes.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".