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Record W4210412949 · doi:10.1016/j.gim.2022.01.001

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC)

2022· article· en· W4210412949 on OpenAlexaff
Peter Horak, Malachi Griffith, Arpad Danos, Beth A. Pitel, Subha Madhavan, Xue‐Lu Liu, Cynthia Chow, Heather Williams, Leigh Carmody, Lisa Barrow-Laing, Damian Rieke, Simon Kreutzfeldt, Albrecht Stenzinger, David Tamborero, Manuela Benary, Padma Sheila Rajagopal, Cristiane M. Ida, Harry Lesmana, Laveniya Satgunaseelan, Jason D. Merker, Michael Tolstorukov, Paulo Vidal Campregher, Jeremy L. Warner, Shruti Rao, Maya Natesan, Haolin Shen, Jeffrey M. Venstrom, Somak Roy, Kayoko Tao, Rashmi Kanagal‐Shamanna, Xinjie Xu, Deborah Ritter, Kym Pagel, Kilannin Krysiak, Adrian M. Dubuc, Yassmine Akkari, Xuan Shirley Li, Jennifer Lee, Ian King, Gordana Raca, Alex H. Wagner, Marylin M. Li, Sharon E. Plon, Shashikant Kulkarni, Obi L. Griffith, Debyani Chakravarty, Dmitriy Sonkin

Bibliographic record

VenueGenetics in Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity Health NetworkBC Cancer Agency
FundersNational Cancer InstituteNational Center for Advancing Translational SciencesNational Human Genome Research InstituteNational Institutes of Health
KeywordsSomatic cellComputational biologyGenomicsGermlineCancerGenomeBiologyGeneticsBioinformaticsMedicineGene

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.203
metaresearch head score (Gemma)0.267
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.203
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2030.267
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0060.013
Bibliometrics0.0200.015
Science and technology studies0.0070.009
Scholarly communication0.0100.003
Open science0.0310.010
Research integrity0.0250.030
Insufficient payload (model declined to judge)0.0040.006

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 head0.066
GPT teacher head0.387
Teacher spread0.321 · 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.

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

Citations239
Published2022
Admission routes1
Has abstractno

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