2024 CUA-KCRNC Expert Report: Management of non-clear cell renal cell carcinoma
Bibliographic record
Abstract
During the 2024 Canadian Kidney Cancer Forum (CKCF) held from February 8-10, 2024, in Toronto, Ontario, Canada, a dedicated session convened an expert panel comprising urologic and medical oncologists, other healthcare professionals, and patient advocates.The purpose of this session was to facilitate a discussion on the management of nccRCC and to develop consensus statements based on the best available evidence.These statements are intended to provide practical guidance to healthcare professionals and medical practitioners in their clinical practices.Prior to the session, draft topic statements were generated by two authors (JG, PR) and subjected to review for clarity and completeness by board members of the Kidney Cancer Research Network of Canada (NB, RB, TF, ST, and LW), as detailed in Table 1.These topics spanned diagnostic imaging, pathologic classification, and genetic considerations, as well as treatment options for patients with localized and metastatic nccRCC, with a focus on papillary and chromophobe RCC.Each topic statement was presented during the session, followed by a period for panel members to
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".