Data Set for the Reporting of Carcinomas of the Cervix: Recommendations From the International Collaboration on Cancer Reporting (ICCR)
Bibliographic record
Abstract
A comprehensive pathologic report is essential for optimal patient management, cancer staging and prognostication. In many countries, proforma reports are used but the content of these is variable. The International Collaboration on Cancer Reporting is an alliance formed by the Royal Colleges of Pathologists of Australasia and the United Kingdom, the College of American Pathologists, the Canadian Partnership Against Cancer and the European Society of Pathology, for the purpose of developing standardized, evidence-based reporting data sets for each cancer site. This will reduce the global burden of cancer data set development and reduplication of effort by different international institutions that commission, publish and maintain standardized cancer-reporting data sets. The resultant standardization of cancer-reporting benefits not only those countries directly involved in the collaboration but also others not in a position to develop their own data sets. We describe the development of an evidence-based cancer data set by the International Collaboration on Cancer Reporting expert panel for the reporting of primary cervical carcinomas and present the "required" and "recommended" elements to be included in the pathology report as well as an explanatory commentary. This data set encompasses the International Federation of Obstetricians and Gynaecologists and Union for International Cancer Control staging systems for cervical neoplasms and the updated World Health Organization classification of gynecologic tumors. The data set also addresses controversial issues such as tumor grading and measurement, including measurement of multifocal carcinomas. The widespread implementation of this data set will facilitate consistent and accurate data collection, comparison of epidemiological and pathologic parameters between different populations, facilitate research, and hopefully result in improved patient management.
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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 | 0.431 | 0.508 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.032 | 0.035 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.018 | 0.012 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".