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Record W4389286008 · doi:10.1016/j.gim.2023.101036

Recommendations for risk allele evidence curation, classification, and reporting from the ClinGen Low Penetrance/Risk Allele Working Group

2023· article· en· W4389286008 on OpenAlexaff
Ryan J. Schmidt, Marcie Steeves, Pınar Bayrak‐Toydemir, Katherine A. Benson, Bradley P. Coe, Laura K. Conlin, Mythily Ganapathi, John Garcia, Michael H. Gollob, Vaidehi Jobanputra, Minjie Luo, Deqiong Ma, Glenn A. Maston, Kelly McGoldrick, Timothy Blake Palculict, Tina Pesaran, Toni I. Pollin, Emily Qian, Heidi L. Rehm, Erin Rooney Riggs, Samantha L.P. Schilit, Panagiotis I. Sergouniotis, Tatiana Tvrdik, Lauren Zec, Wenying Zhang, Matthew S. Lebo, Alicia B. Byrne, Amanda B. Spurdle, Blake Palculict, Ma Deqiong, Elaine Lyon, Emily Groopman, Erik G. Puffenberger, Fergus J. Couch, Hannah Dziadzio, James Harraway, Jessica L. Mester, Jordan Lerner‐Ellis, Kayleigh Avello, Marcy E. Richardson, Melissa Kelly, Nifang Niu, Sarah Richards, Wuyan Chen, Yuxin Fan

Bibliographic record

VenueGenetics in Medicine · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsSinai Health SystemToronto General HospitalUniversity of Toronto
FundersNational Cancer InstituteNational Human Genome Research InstituteNational Institutes of Health
KeywordsPenetranceAlleleGeneticsDiseaseHarmonizationPopulationAllele frequencyBiologyMedicineEnvironmental healthGenePhenotypePathology

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)0.254
metaresearch head score (Gemma)0.589
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2540.589
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0080.020
Bibliometrics0.0200.014
Science and technology studies0.0030.005
Scholarly communication0.0120.010
Open science0.0190.011
Research integrity0.0290.029
Insufficient payload (model declined to judge)0.0180.015

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 head0.130
GPT teacher head0.367
Teacher spread0.237 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainReporting
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

Citations69
Published2023
Admission routes1
Has abstractno

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