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Record W2039973990 · doi:10.5489/cuaj.1496

Canadian guideline on genetic screening for hereditary renal cell cancers

2013· article· en· W2039973990 on OpenAlexafffundvenueabout
M. Neil Reaume, Gail E. Graham, Eva Tomiak, Suzanne Kamel‐Reid, Michael A.S. Jewett, Georg A. Bjarnason, Normand Blais, Melanie Care, Darryl Drachenberg, Craig Gedye, Ronald Grant, Daniel Y.C. Heng, Anil Kapoor, Christian Kollmannsberger, Jean‐Baptiste Lattouf, Eamonn R. Maher, Arnim Pause, Dean Ruether, Denis Soulières, Simon Tanguay, Sandra Turcotte, Philippe D. Violette, Lori Wood, Joan Basiuk, Stephen E. Pautler

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsBC Cancer AgencyMcMaster UniversitySunnybrook Health Science CentreWestern UniversityUniversité de MonctonUniversity of TorontoOttawa HospitalUniversity Health NetworkPrincess Margaret Cancer CentreUniversity of CalgaryCentre Hospitalier de l’Université de MontréalQueen Elizabeth II Health Sciences CentreChildren's Hospital of Eastern OntarioUniversity of ManitobaMcGill UniversityUniversity of Ottawa
FundersKidney Foundation of Canada
KeywordsGuidelineMedicineReferralGenetic testingMedical diagnosisFamily medicineGenetic counselingGenetic diagnosisIntensive care medicineInternal medicinePathologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Hereditary renal cell cancer (RCC) is an ideal model for germline genetic testing. We propose a guideline of hereditary RCC specific criteria to suggest referral for genetic assessment. METHODS: A review of the literature and stakeholder resources for existing guidelines or consensus statements was performed. Referral criteria were developed by expert consensus. RESULTS: The criteria included characteristics for patients with RCC (age ≤45 years, bilateral or multifocal tumours, associated medical conditions and non-clear cell histologies with unusual features) and for patients with or without RCC, but a family history of specific clinical or genetic diagnoses. CONCLUSIONS: This guideline represents a practical RCC-specific reference to allow healthcare providers to identify patients who may have a hereditary RCC syndrome, without extensive knowledge of each syndrome. RCC survivors and their families can also use the document to guide their discussions with healthcare providers about their need for referral. The criteria refer to the most common hereditary renal tumour syndromes and do not represent a comprehensive or exclusive list. Prospective validation of the criteria is warranted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations38
Published2013
Admission routes4
Has abstractyes

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