The Vienna classification of gastrointestinal epithelial neoplasia
Bibliographic record
Abstract
BACKGROUND: Use of the conventional Western and Japanese classification systems of gastrointestinal epithelial neoplasia results in large differences among pathologists in the diagnosis of oesophageal, gastric, and colorectal neoplastic lesions. AIM: To develop common worldwide terminology for gastrointestinal epithelial neoplasia. METHODS: Thirty one pathologists from 12 countries reviewed 35 gastric, 20 colorectal, and 21 oesophageal biopsy and resection specimens. The extent of diagnostic agreement between those with Western and Japanese viewpoints was assessed by kappa statistics. The pathologists met in Vienna to discuss the results and to develop a new consensus terminology. RESULTS: The large differences between the conventional Western and Japanese diagnoses were confirmed (percentage of specimens for which there was agreement and kappa values: 37% and 0.16 for gastric; 45% and 0.27 for colorectal; and 14% and 0.01 for oesophageal lesions). There was much better agreement among pathologists (71% and 0.55 for gastric; 65% and 0.47 for colorectal; and 62% and 0.31 for oesophageal lesions) when the original assessments of the specimens were regrouped into the categories of the proposed Vienna classification of gastrointestinal epithelial neoplasia: (1) negative for neoplasia/dysplasia, (2) indefinite for neoplasia/dysplasia, (3) non-invasive low grade neoplasia (low grade adenoma/dysplasia), (4) non-invasive high grade neoplasia (high grade adenoma/dysplasia, non-invasive carcinoma and suspicion of invasive carcinoma), and (5) invasive neoplasia (intramucosal carcinoma, submucosal carcinoma or beyond). CONCLUSION: The differences between Western and Japanese pathologists in the diagnostic classification of gastrointestinal epithelial neoplastic lesions can be resolved largely by adopting the proposed terminology, which is based on cytological and architectural severity and invasion status.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".