Worldwide Esophageal Cancer Collaboration: pathologic staging data
Bibliographic record
Abstract
We report data-simple descriptions of patient characteristics, cancer categories, and non-risk-adjusted survival-for patients with pathologically staged cancer of the esophagus and esophagogastric junction after resection or ablation with no preoperative therapy from the Worldwide Esophageal Cancer Collaboration (WECC). Thirty-three institutions from six continents submitted de-identified data using standard definitions: demographics, comorbidities, clinical cancer categories, and all-cause mortality from first management decision. Of 13,300 patients, 5,631 had squamous cell carcinoma, 7,558 adenocarcinoma, 85 adenosquamous carcinoma, and 26 undifferentiated carcinoma. Patients were older (62 years) men (80%) with normal body mass index (51%), little weight loss (1.8 kg), 0-2 ECOG performance status (83%), and a history of smoking (70%). Cancers were pT1 (24%), pT2 (15%), pT3 (50%), pN0 (52%), pM0 (93%), and pG2-G3 (78%); most involved distal esophagus (71%). Non-risk-adjusted survival for both squamous cell carcinoma and adenocarcinoma was monotonic and distinctive across pTNM. Survival was more distinctive for adenocarcinoma than squamous cell carcinoma when pT was ordered by pN. Survival for pTis-1 adenocarcinoma was better than for squamous cell carcinoma, although monotonic and distinctive for both. WECC pathologic staging data is improved over that of the 7th edition, with more patients studied and patient and cancer variables collected. These data will be the basis for the 8th edition cancer staging manuals following risk adjustment for patient, cancer, and treatment characteristics, and should direct 9th edition data collection. However, the role of pure pathologic staging as the principal point of reference for esophageal cancer staging is waning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".