Management of kidney cancer: Canadian Kidney Cancer Forum Consensus Update
Bibliographic record
Abstract
4 K idney cancer, predominantly renal cell carcinoma (RCC), remains the most lethal genitourinary malignancy and kills more than 1500 Canadians a year. 1 In 2008, the first Canadian Kidney Cancer Forum was held and a clinical management consensus was developed and reported.2 In January 2009, a second forum was convened near Toronto, Ont., with multidisciplinary attendees, this time including researchers and the Board of Kidney Cancer Canada (www.kidneycancercanada.org).During the conference, the 2008 consensus was reviewed and updated using the same process.This report is a brief overview of those updates using the same format and should be read in conjunction with that report. 2 Explanatory text has been omitted in this version if no significant new information has become available.This document represents the current consensus of the attendees who are now the founding members of a new organization, the Kidney Cancer Research Network of Canada.This network will be the subject of future discussion and reports but is intended to begin as an informal, inclusive virtual network of researchers; clinical, translational and basic, with a common interest in kidney cancer research.The forum addressed strategies for kidney cancer control in Canada, which included planning for a Canadian Kidney Cancer Database, collaboration to validate follow-up guidelines defined by the Canadian Urological Association, a coordinated approach to genetic testing for genes relevant to kidney cancer and the initiation of a process to define quality indicators for the management of kidney cancer as part of an overall strategy to define centres of excellence.3 The attendees are developing a position paper on kidney cancer education, research and treatment in Canada that will include the full consensus combining this update with the 2008 statements.Finally, the forum was held with Kidney Cancer Canada whose members continue to monitor access to targeted therapeutic agents across the country.Attendees reaffirmed their support for equal access to these treatments for all Canadians in whom there is a clinical rationale.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".