Dataset for reporting of thymic epithelial tumours: recommendations from the International Collaboration on Cancer Reporting (ICCR)
Bibliographic record
Abstract
Aims: The International Collaboration on Cancer Reporting (ICCR) is a not-for-profit organization formed by the Royal Colleges of Pathologists of Australasia and the United Kingdom, the College of American Pathologists, the Canadian Association of Pathologists-Association Canadienne des Pathologists in association with the Canadian Partnership Against Cancer, and the European Society of Pathology. Its goal is to produce standardized, internationally agreed, evidence-based datasets for use throughout the world. Methods and results: This article describes the development of a cancer dataset by the multidisciplinary ICCR expert panel for the reporting of thymic epithelial tumours. The dataset includes 'required' (mandatory) and 'recommended' (non-mandatory) elements, which are validated by a review of current evidence and supported by explanatory text. Seven required elements and 12 recommended elements were agreed by the international dataset authoring committee to represent the essential information for the reporting of thymic epithelial tumours. Conclusions: The use of an internationally agreed, structured pathology dataset for reporting thymic tumours provides all of the necessary information for optimal patient management, facilitates consistent and accurate data collection, and provides valuable data for research and international benchmarking. The dataset also provides a valuable resource for those countries and institutions that are not in a position to develop their own datasets.
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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 | 0.240 | 0.406 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".