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Record W2346043562 · doi:10.1016/j.ajhg.2016.02.024

Evaluation of ACMG-Guideline-Based Variant Classification of Cancer Susceptibility and Non-Cancer-Associated Genes in Families Affected by Breast Cancer

2016· article· en· W2346043562 on OpenAlexafffund
Kara N. Maxwell, Steven N. Hart, Joseph Vijai, Kasmintan A. Schrader, Thomas P. Slavin, Tinu Thomas, Bradley Wubbenhorst, Vignesh Ravichandran, Raymond M. Moore, Chunling Hu, Lucia Guidugli, Brandon M. Wenz, Susan M. Domchek, Mark E. Robson, Csilla I. Szabo, Susan L. Neuhausen, Jeffrey N. Weitzel, Kenneth Offit, Fergus J. Couch, Katherine L. Nathanson

Bibliographic record

VenueThe American Journal of Human Genetics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesNational Cancer InstituteCanadian Institutes of Health ResearchNational Institutes of HealthBC Cancer FoundationU.S. Department of DefenseBreast Cancer Research FoundationAndrew Sabin Family FoundationMichael Smith Health Research BCCommonwealth of PennsylvaniaMemorial Sloan-Kettering Cancer CenterAmerican Society of Clinical OncologyMayo ClinicAbramson Family Cancer Research InstituteAvon Foundation for WomenAmerican Cancer SocietySusan G. Komen for the Cure
KeywordsBreast cancerCancerExome sequencingGeneticsLocus (genetics)Medical geneticsExomeGenetic testingGeneMutationMedicineBiologyOncologyBioinformaticsInternal medicine

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)0.018
metaresearch head score (Gemma)0.050
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.050
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.340
Teacher spread0.318 · 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

Citations132
Published2016
Admission routes2
Has abstractno

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