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Record W2516242315 · doi:10.1101/072892

CIViC: A knowledgebase for expert-crowdsourcing the clinical interpretation of variants in cancer

2016· preprint· en· W2516242315 on OpenAlexaff
Malachi Griffith, Nicholas C. Spies, Kilannin Krysiak, Adam Coffman, Joshua F. McMichael, Benjamin J. Ainscough, Damian Rieke, Arpad Danos, Lynzey Kujan, Cody A. Ramirez, Alex H. Wagner, Zachary L. Skidmore, Connor J. Liu, Martin Jones, Rachel L Bilski, Robert Lesurf, Erica K. Barnell, Nakul M. Shah, Melika Bonakdar, Lee Trani, Matthew K. Matlock, Avinash Ramu, Katie M. Campbell, Gregory C. Spies, Aaron Graubert, Karthik Gangavarapu, James M. Eldred, David E. Larson, Jason Walker, Benjamin M. Good, Chunlei Wu, Andrew I. Su, Rodrigo Dienstmann, Steven J.M. Jones, Ron Bose, David H. Spencer, Lukas D. Wartman, Richard K. Wilson, Elaine R. Mardis, Obi L. Griffith

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2016
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer Agency
FundersNational Human Genome Research InstituteNational Institutes of HealthNational Cancer InstituteBundesministerium für Bildung und Forschung
KeywordsCrowdsourcingInterpretation (philosophy)Relevance (law)Open sourceAnnotationData scienceComputer scienceWorld Wide WebPolitical scienceArtificial intelligenceSoftware

Abstract

fetched live from OpenAlex

Abstract CIViC is an expert crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer ( www.civicdb.org ) describing the therapeutic, prognostic, and diagnostic relevance of inherited and somatic variants of all types. CIViC is committed to open source code, open access content, public application programming interfaces (APIs), and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

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)0.013
metaresearch head score (Gemma)0.075
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: Software · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.075
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.009
Science and technology studies0.0030.002
Scholarly communication0.0060.003
Open science0.0050.012
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0310.018

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 head0.298
Teacher spread0.277 · 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
GenreSoftware

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

Citations8
Published2016
Admission routes1
Has abstractyes

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