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Record W2286854579 · doi:10.5489/cuaj.568

Canadian Kidney Cancer Forum 2008

2013· article· en· W2286854579 on OpenAlexaffvenueabout
Michael A.S. Jewett, Jennifer J. Knox, Christian K. Kollmansberger, Joan Basiuk

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity Health Network
Fundersnot available
KeywordsKidney cancerCancerMedicineGenitourinary systemHuman kidneyDiseaseFamily medicinePathologyKidneyInternal medicine

Abstract

fetched live from OpenAlex

In Canada, at least 4500 people are diagnosed with kidney cancer, and 1 in 3 die of the disease. It is the most lethal genitourinary cancer, and its incidence is increasing. Unlike most human cancers, there is considerable knowledge about specific genetic changes in these cancers. Major advances in treatment are being made owing to new systemic targeted therapies as well as less morbid technologies for localized cancers. The first Canadian Kidney Cancer Forum was held Jan. 31–Feb. 2, 2008, in Mont Tremblant, Quebec, to review these developments and identify opportunities for Canadians. The primary meeting objectives were achieved and included: Achievement of consensus among expert professionals and patients on the management of patients with localized, locally advanced and metastatic renal cell carcinoma, as well as the in the areas of pathology and imaging. Over 3 days, a series of sessions were held to address the different stages of disease and issues. In this issue, we present the consensus statements. Key references in each area were provided by experts and were graded using a modified version of the Oxford Levels of Evidence. During the conference, experts in each area presented, followed by an opportunity for questions and discussion. At the end of the forum, overviews were presented, and participants voted on draft statements developed during the sessions. Stimulation of clinical research in kidney cancer including opportunities for national and international collaboration. To achieve this, the formation of a research network was proposed with the suggested name, Kidney Cancer Research Network Canada. Definition of educational needs to improve kidney cancer care in Canada. This included discussing a national kidney cancer database that will be developed. Initiating the development of a cancer control strategy for kidney cancer to ensure all Canadians have equal access to care to attempt to develop a national approach to the management of this cancer. The Forum was endorsed by the Canadian Urological Association, the Canadian Association of Medical Oncologists and the Canadian Association of Radiation Oncologists, and was supported by grants from the Canadian Institutes of Health Research, the National Cancer Institute of Canada, the Canadian Cancer Society, the Canadian Partnership Against Cancer, several provincial cancer agencies (Cancer Care Ontario, Cancer Care Manitoba, Cancer Care Nova Scotia, Alberta Cancer Foundation) and the Princess Margaret Hospital Foundation, and industry. These groups had been identified by the steering committee as ones that had demonstrated expertise, interest or experience in clinical or research aspects of kidney cancer. They were joined by a small number of invited participants from Europe and the United States to bring an international perspective to the process. Dr. George Browman, representing the Canadian Partnership Against Cancer, contributed expertise in the development and use of guidelines for cancer care. Overall, the participants believe that the forum will have a significant impact on the care of Canadians afflicted with kidney cancer. Given the enormous success of this first meeting, planning for a second meeting in 2009 has commenced.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.299
Threshold uncertainty score0.601

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0060.001
Scholarly communication0.0060.002
Open science0.0030.003
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.1560.034

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.

Opus teacher head0.012
GPT teacher head0.222
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations6
Published2013
Admission routes3
Has abstractyes

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