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Record W4392588987 · doi:10.1016/j.gimo.2024.100984

P103: Operationalizing structured curated scientific literature (CIViC and Hypothesis) in developing gene-specific recommendations of the ClinGen VHL Variant Curation Expert Panel

2024· article· en· W4392588987 on OpenAlexaff
Deborah Ritter, Chansonette Badduke, Michael J. Anderson, Arpad Danos, Kurston Doonanco, Kirsten M. Farncombe, Bailey Gallinger, Rachel H. Giles, Malachi Griffith, Obi L. Griffith, Carolyn Horton, Kathleen S. Hruska, Hio Chung Kang, Kilannin Krysiak, Minjie Luo, Jerry Machado, Eamonn R. Maher, Kelly McGoldrick, Tina Pesaran, Neta Pipko, Sarah Ridd, Jason Saliba, Clare Sheen, Raymond Y. Kim

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsOperationalizationComputational biologyBiologyGeneticsData scienceComputer scienceEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Von Hippel-Lindau (VHL) disease is a hereditary tumor predisposition syndrome caused by variants in the VHL gene and has an incidence of ∼1/36,000. The Clinical Genome Resource (ClinGen) VHL Variant Curation Expert Panel (VCEP) recently specified ACMG evidence codes for VHL and aims to be an approved expert panel. The VHL VCEP is unique in that it has two large structured sets of VHL scientific literature curated through other efforts: Clinical Interpretation of Variants in Cancer (CIViC, www.civicdb.org) with >630 VHL variants representing >428 papers, and Hypothesis (web.hypothes.is) with >8700 structured annotations from VHL clinical literature.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.365
GPT teacher head0.470
Teacher spread0.105 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

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