Actionable Locoregional Relapses after Therapy of Localized Esophageal Cancer: Insights from a Large Cohort
Bibliographic record
Abstract
OBJECTIVE: The goal of surveillance after therapy of localized esophageal cancer (LEC) is to identify actionable relapses amenable to salvage; however, the current surveillance algorithms are not optimized. We report on a large cohort of LEC patients with actionable locoregional relapses (LRRs). METHODS: Between 2000 and 2013, 127 (denominator = 752) patients with actionable LRR were identified. Histologic/cytologic confirmation was the gold standard. All surveillance tools (imaging, endoscopy, fine needle aspiration) were assessed. RESULTS: Most patients were men (89%), had adenocarcinoma (79%), and had no new symptoms (72%) when diagnosed with LRR. In trimodality patients, endoscopic confirmation of positron emission tomography-computed tomography-suspected LRR occurred in only 44%, and 56% required additional tools (e.g., fine needle aspiration). Alternatively, in bimodality patients, endoscopy confirmed LRRs in 81%. Trimodality patients had a higher risk of subsequent LRR/distant metastases after the first LRR than the bimodality patients (p = 0.03). In all patients, 78% of the subsequent relapses were distant. For patients who were salvaged, survival was significantly prolonged (50.6 vs. 25.1 months, p < 0.01). CONCLUSIONS: Patients live longer after successful salvage of the LRR than if salvage is not possible. After LRR, patients have a high risk of subsequent distant metastasis and whether the second relapse is local or distant, survival is uniformly poor.
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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 | 0.001 | 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".