Data set for reporting of ovary, fallopian tube and primary peritoneal carcinoma: Recommendations from the International Collaboration on Cancer Reporting (ICCR)
Bibliographic record
Abstract
© 2015 USCAP, Inc All rights reserved. A comprehensive pathological report is essential for optimal patient management, cancer staging and prognostication. In many countries, proforma reports are used but these vary in their content. The International Collaboration on Cancer Reporting (ICCR) is an alliance formed by the Royal College of Pathologists of Australasia, the Royal College of Pathologists of the United Kingdom, the College of American Pathologists, the Canadian Partnership Against Cancer and the European Society of Pathology, with the aim of developing an evidence-based reporting data set 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 standardised cancer reporting data sets. The resultant standardisation of cancer reporting will benefit 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 a cancer data set by the ICCR expert panel for the reporting of primary ovarian, fallopian tube and peritoneal carcinoma and present the 'required' and 'recommended' elements to be included in the report with an explanatory commentary. This data set encompasses the recent International Federation of Obstetricians and Gynaecologists staging system for these neoplasms and the updated World Health Organisation Classification of Tumours of the Female Reproductive Organs. The data set also addresses issues about site assignment of the primary tumour in high-grade serous carcinomas and proposes a scoring system for the assessment of tumour response to neoadjuvant chemotherapy. The widespread implementation of this data set will facilitate consistent and accurate data collection, comparison of epidemiological and pathological parameters between different populations, facilitate research and hopefully will result in improved patient management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".