Data Set for the Reporting of Carcinoma of the Renal Pelvis and Ureter—Nephroureterectomy and Ureterectomy Specimens
Bibliographic record
Abstract
Cancer reporting guidelines have been developed and utilized in many countries throughout the world. The International Collaboration on Cancer Reporting (ICCR), through an alliance of colleges and other pathology organizations in Australasia, United Kingdom, Ireland, Europe, USA, and Canada, has developed comprehensive standardized data sets to provide for global usage and promote uniformity in cancer reporting. Structured reporting facilitates provision of all necessary information, which ensures accurate and comprehensive data collection, with the ultimate aim of improving cancer diagnostics and treatment. The data set for primary carcinoma of the renal pelvis and ureter treated with nephroureterectomy or ureterectomy had input from an expert panel of international uropathologists. This data set was based on current evidence-based practice and incorporated information from the 2016 fourth edition of the World Health Organization (WHO) Bluebook on tumors of the urinary and male genital systems and the 2017 American Joint Committee on Cancer (AJCC) TNM staging eighth edition. This protocol applies to both noninvasive and invasive carcinomas in these locations. Reporting elements are considered to be essential (required) or nonessential (recommended). Required elements include operative procedure, specimens submitted, tumor location, focality and size, histologic tumor type, subtype/variant of urothelial carcinoma, WHO grade, extent of invasion, presence or absence of vascular invasion, status of the resection margins and lymph nodes and pathologic stage. The data set provides a detailed template for the collection of data and it is anticipated that this will facilitate appropriate patient management with the potential to foster collaborative research internationally.
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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 | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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 source (direct Gemma or distilled Codex), 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".