Management of advanced kidney cancer: Canadian Kidney Cancer Forum consensus update
Bibliographic record
Abstract
Kidney Cancer][3][4] Kidney cancer, predominantly renal cell carcinoma (RCC), is the most lethal genitourinary malignancy and kills more than 1750 Canadians a year. 5 The overall incidence is increasing by 2% per year for unknown reasons, with most new cases being small renal masses.For almost a decade, targeted systemic therapies have been available and have been integrated into clinical practice with evolving experience.Preservation of kidney function with widespread adoption of partial nephrectomy is a focus of treatment of early stage disease.These and other advances have revolutionized care and stimulated research and discovery.[4]6,7 Five previous forums were held in 2008, 2009, 2011, 2013, and 2014.As before, the 2015 meeting was small, by invitation and attended by survivors, caregivers, expert clinicians and researchers in fields relevant to kidney cancer care.The attendees included representatives of Kidney Cancer Canada (www.kidneycancercanada.org). 8 During the conference, prior management consensus statements were reviewed and updated using the same process.This report is an update of the advanced disease management component of the consensus published in 2013. 4The Forum again addressed strategies for kidney cancer control in Canada, which included updates from the now operational Canadian Kidney Cancer Information System (CKCis), as well as reports back from the KCRNC main working groups.These KCRNC groups are working on initiatives in four major domains to improve kidney cancer patient care: (1) personalized medicine; (2) quality care initiatives; (3) survivorship, and (4) genetics.Prior to the start of the Forum, satellite meetings of various working groups also took place, including a new initiative known as the James Lind Alliance (JLA) working group.The JLA is a non-profit organization founded in 2004 that brings together patients, clinicians, and caregivers and through a rigorous process identifies the top 10 uncertainties, or unanswered questions, about a given medical problem. 9he working group established the top 10 uncertainties for kidney cancer management in Canada and we believe this is the first time such an undertaking for kidney cancer has ever happened worldwide and will help inform the working groups on research priorities.This consensus statement pertains to the management of advanced disease.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".