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Record W4385389163 · doi:10.1016/j.gim.2023.100947

The landscape of reported VUS in multi-gene panel and genomic testing: Time for a change

2023· article· en· W4385389163 on OpenAlexaff
Heidi L. Rehm, Joseph T. Alaimo, Swaroop Aradhya, Pınar Bayrak‐Toydemir, Hunter Best, Rhonda Brandon, Jillian G. Buchan, Elizabeth Chao, Elaine Chen, Jacob Clifford, Ana S.A. Cohen, Laura K. Conlin, Soma Das, Kyle Davis, Daniela del Gaudio, Florencia Del Viso, Christina DiVincenzo, Marcia Eisenberg, Lucia Guidugli, Monia Hammer, Steven M. Harrison, Kathryn E. Hatchell, Lindsay Havens Dyer, Lily Hoang, James Holt, Vaidehi Jobanputra, Izabela Karbassi, Hutton M. Kearney, Melissa Kelly, Jacob M. Kelly, Michelle L. Kluge, Timothy Komala, Paul Kruszka, Lynette Lau, Matthew S. Lebo, Christian R. Marshall, Dianalee McKnight, Kirsty McWalter, Yan Meng, Narasimhan Nagan, Christian S. Neckelmann, Nir Neerman, Zhiyv Niu, Vitoria Paolillo, Sarah A Paolucci, Denise Perry, Tina Pesaran, Kelly Radtke, Kristen Rasmussen, Kyle Retterer, Carol Saunders, Elizabeth Spiteri, Christine M. Stanley, Anna Szuto, Ryan J. Taft, Isabelle Thiffault, Brittany C. Thomas, Amanda Thomas‐Wilson, Erin Thorpe, Timothy Tidwell, Meghan C. Towne, Hana Zouk, Christian Marshall, Linyan Meng, Euan A. Ashley, Ghunwa Nakouzi, Wei Shen, Stephen F. Kingsmore

Bibliographic record

VenueGenetics in Medicine · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersNational Human Genome Research InstituteMcKnight Foundation
KeywordsExome sequencingExomeGenetic testingComputational biologyGeneGenomeGeneticsMedicineBioinformaticsBiologyMutation

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)0.053
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.090
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0070.006
Science and technology studies0.0020.006
Scholarly communication0.0070.016
Open science0.0050.005
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0060.001

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 head0.096
GPT teacher head0.315
Teacher spread0.219 · 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 designObservational
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

Citations129
Published2023
Admission routes1
Has abstractno

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