Data Sets for the Reporting of Head and Neck Tumors: Second Edition Update From the International Collaboration of Cancer Reporting
Bibliographic record
Abstract
Context.—: The International Collaboration on Cancer Reporting is a not-for-profit organization whose goal is to develop evidence-based, internationally agreed standardized data sets for each anatomic site to be used throughout the world. Objective.—: To update the changes in the 2nd edition of the data set suite, including carcinomas of the hypopharynx, larynx and trachea, major salivary glands, nasal cavity and paranasal sinuses, oropharynx and nasopharynx, and oral cavity, and ear and temporal bone tumors, malignant odontogenic tumors, mucosal melanomas of the head and neck, and nodal excisions and neck dissection specimens. Design.—: International consensus by expert data set authoring committees, especially authors of the World Health Organization head and neck tumor classification. Results.—: The unique features have been updated based on current research and developments in histologic classification and standardized reporting guidelines. Separation between core and noncore elements is based on data meaningful to prognosis and stratification. The changes are in conjunction with publication of the 5th edition of the World Health Organization head and neck tumor classification. Conclusions.—: Increased harmonization of reporting and benchmarking improves patient outcomes and international collaborative research.
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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 | 0.262 | 0.402 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.028 | 0.042 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".