Data Set for the Reporting of Carcinomas of the Vulva: Recommendations From the International Collaboration on Cancer Reporting (ICCR)
Bibliographic record
Abstract
A cogent and comprehensive pathologic report is essential for optimal patient management, cancer staging, and prognostication. This article details the International Collaboration on Cancer Reporting (ICCR) process and the development of the vulval carcinoma reporting data set. It describes the "core" and "noncore" elements to be included in pathology reports for vulval carcinoma, inclusive of clinical, macroscopic, microscopic, and ancillary testing considerations. It provides definitions and commentary for the evidence and/or consensus-based deliberations for each element included in the data set. The commentary also discusses controversial issues, such as p16/human papillomavirus testing, tumor grading and measurements, as well as elements that show promise and warrant further evidence-based study. A summary and discussion of the updated vulval cancer staging system by the International Federation of Obstetricians and Gynaecologists (FIGO) in 2021 is also provided. We hope the widespread implementation of this data set will facilitate consistent and accurate reporting, data collection, comparison of epidemiological and pathologic parameters between different populations, facilitate research, and serve as a platform to improve patient outcomes.
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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 | 0.501 | 0.603 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.018 | 0.015 |
| Research integrity | 0.016 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".