Data set for the reporting of carcinoma of renal tubular origin: recommendations from the International Collaboration on Cancer Reporting (<scp>ICCR</scp>)
Bibliographic record
Abstract
AIMS: The International Collaboration on Cancer Reporting (ICCR) has provided detailed data sets based upon the published reporting protocols of the Royal College of Pathologists, the Royal College of Pathologists of Australasia and the College of American Pathologists. METHODS AND RESULTS: The data set for carcinomas of renal tubular origin treated by nephrectomy was developed to provide a minimum structured reporting template suitable for international use, and incorporated recommendations from the 2012 Vancouver Consensus Conference of the International Society of Urological Pathology (ISUP) and the fourth edition of the World Health Organisation Bluebook on tumours of the urinary and male genital systems published in 2016. Reporting elements were divided into those, which are required and recommended components of the report. Required elements are: specimen laterality, operative procedure, attached structures, tumour focality, tumour dimension, tumour type, WHO/ISUP grade, sarcomatoid/rhabdoid morphology, tumour necrosis, extent of invasion, lymph node status, surgical margin status, AJCC TNM staging and co-existing pathology. Recommended reporting elements are: pre-operative treatment, details of tissue removed for experimental purposes prior to submission, site of tumour(s) block identification key, extent of sarcomatoid and/or rhabdoid component, extent of necrosis, presence of tumour in renal vein wall, lymphovascular invasion and lymph node status (size of largest focus and extranodal extension). CONCLUSIONS: It is anticipated that the implementation of this data set in routine clinical practice will inform patient treatment as well as provide standardised information relating to outcome prediction. The harmonisation of data reporting should also facilitate international research collaborations.
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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 | 0.208 | 0.372 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".