MétaCan
Menu
Back to cohort
Record W2020367267 · doi:10.3747/co.v18i4.779

Canadian Expert Group Consensus Recommendations: KRAS Testing in Colorectal Cancer

2011· article· en· W2020367267 on OpenAlexaffvenueabout
F. Aubin, Sharlene Gill, Ronald L. Burkes, Brian Colwell, Suzanne Kamel‐Reid, Sheryl Koski, Aaron Pollett, Benoît Samson, Mustapha Tehfé, Ralph Wong, Sean Young, Denis Soulières

Bibliographic record

VenueCurrent Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsCancerCare ManitobaPrincess Margaret Cancer CentreNova Scotia Cancer CentreBC Cancer AgencyHôpital Charles-Le MoyneCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsKRASMedicineColorectal cancerPanitumumabOncologyInternal medicineConsensus conferenceCancerCetuximabFamily medicine

Abstract

fetched live from OpenAlex

Monoclonal antibodies against the epidermal growth factor receptor (anti-egfr) when used in the treatment of metastatic colorectal cancer are associated with improved survival. Patients whose tumours harbor a KRAS mutation in codon 12 or 13 have been shown not to benefit from anti-egfr antibodies. The importance of KRAS mutation status in the management of patients with metastatic colorectal cancer has led to the elaboration of Canadian consensus recommendations on KRAS testing, with the aim of standardizing practice across Canada and reconciling testing access with the clinical demand for testing. The present guidelines were developed at a Canadian consensus meeting held in Montreal in April 2010. The best available evidence and expertise were used to develop recommendations for various aspects of KRAS testing, including indications and timing for testing, sample requirements, recommendations for reporting requirements, and acceptable turnaround times.

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)0.059
metaresearch head score (Gemma)0.142
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: Methods · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.142
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0090.009
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0110.003
Research integrity0.0130.009
Insufficient payload (model declined to judge)0.0080.004

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 head0.259
GPT teacher head0.432
Teacher spread0.173 · 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
GenreMethods

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

Citations23
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueCurrent OncologySame topicColorectal Cancer Treatments and StudiesFrench-language works237,207