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Record W4324117094 · doi:10.1016/j.gimo.2023.100098

P079: ClinGen Somatic and CIViC collaborate to comprehensively evaluate somatic variants in cancer*

2023· article· en· W4324117094 on OpenAlexaff
Jason Saliba, Arpad Danos, Kilannin Krysiak, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Cameron J. Grisdale, Ian P. King, Shamini Selvarajah, Xinjie Xu, Rashmi Kanagal‐Shamanna, Laveniya Satgunaseelan, David Meredith, Mark I. Evans, Charles G. Mullighan, Yassmine Akkari, Gordana Raca, Angshumoy Roy, Alex H. Wagner, Ramaswamy Govindan, Obi L. Griffith, Malachi Griffith

Bibliographic record

VenueGenetics in Medicine Open · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsSomatic cellBiologyGeneticsPsychologyGene

Abstract

fetched live from OpenAlex

The comprehensive evaluation of somatic variants in cancer requires consensus interpretation of their potential clinical significance (diagnosis, prognosis, and treatment response) and oncogenicity. To aid precision medicine through public interpretations, a multifaceted collaborative effort is required to bring together a community, structured guidance, and a public platform. The over 200 multi-disciplinary experts in the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) provide a community that develops data curation guidelines and standards and curates evidence to determine the clinical significance and oncogenicity of somatic alterations in cancer.

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)0.045
metaresearch head score (Gemma)0.031
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: none
Teacher disagreement score0.057
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0060.003
Open science0.0030.010
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0570.030

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 head0.059
GPT teacher head0.389
Teacher spread0.330 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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