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Data sharing as a national quality improvement program: reporting on BRCA1 and BRCA2 variant-interpretation comparisons through the Canadian Open Genetics Repository (COGR)
Matthew S. Lebo, Kathleen-Rose Zakoor, Kathy Chun, Marsha Speevak, John S. Waye, M. Elizabeth McCready, Jillian S. Parboosingh, Ryan E. Lamont, Harriet Feilotter, Ian Bosdet, Tracy Tucker, Sean Young, Aly Karsan, George S. Charames, Ronald Agatep, Elizabeth Spriggs, Caitlin Chisholm, Nasim Vasli, Hussein Daoud, Olga Jarinova, Robert Tomaszewski, Stacey Hume, Sherryl A. Taylor, Mohammad R. Akbari, Jordan Lerner‐Ellis, Ron Agatep, Peter Ainsworth, Melyssa Aronson, Raveen Basran, André Blavier, Andrea Blumenthal, Kym M. Boycott, Michael Brudno, Kathleen Buckley, Jodi Campbell, Philippe M. Campeau, Melanie Care, Nancy Carson, Ronald Carter, David Chitayat, George Chong, E. Chouinard, Kenneth J. Craddock, Roderick Docking, Andrea Eisen, Hanna Faghfoury, Sandra A. Farrell, Bridget A. Fernandez, Marc Fiume, Cynthia Forster‐Gibson, Jan M. Friedman, William D. Foulkes, Peter Goodhand, Jessica Gu, Robert A. Hegele, Spring Holter, Sheri Horsburgh, Lauren Hughes, Franny Jewett, Anne Junker, Sam Khalouei, Joan H.M. Knoll, Elena Kolomeitz, Bartha Maria Knoppers, Georges Maire, Christian R. Marshall, Grant Mitchell, Michael Moorhouse, Chantal F. Morel, Tanya N. Nelson, Abdul Noor, Brian D. O’Connor, Darren D. O’Rielly, B. F. Francis Ouellette, Hilary Racher, Peter C. Ray, Heidi L. Rehm, Christie Riddell, Jean‐Baptiste Rivière, David S. Rosenblatt, Guy A. Rouleau, Andrea Ruchon, Peter Sabatini, Bekim Sadiković, Kara Semotiuk, Stephen W. Scherer, Cheryl Shuman, Josh Silver, Katherine A. Siminovitch, Lesley Solomon-Izsak, Jean‐François Soucy, James Stavropoulos, Lincoln Stein, Rhonda Tannenbaum, Deborah Terespolsky, Richard F. Wintle, Beatrix Wong, Nora Wong, Marina Wang, Nicholas A. Watkins, Shana White, Michael O. Woods, Philip Wyatt
Bibliographic record
VenueGenetics in Medicine · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsOntario Institute for Cancer ResearchUniversity of AlbertaChildren's Hospital of Eastern OntarioShared HealthSinai Health SystemBC Cancer AgencyQueen's UniversityAlberta Children's HospitalLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of CalgaryUniversity of TorontoHamilton Health SciencesWomen's College HospitalCredit Valley HospitalUniversity of ManitobaTrillium Health CentreHamilton Regional Laboratory Medicine ProgramNorth York General Hospital
FundersCanadian Institutes of Health ResearchGovernment of CanadaOntario GenomicsOntario Genomics InstituteGenome Canada
KeywordsTier 2 networkTier 1 networkUploadMedicineQuality (philosophy)MEDLINEComputer scienceBiologyWorld Wide Web
Abstract
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 distilled prediction
Teacher imitationNot 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.211
Threshold uncertainty score0.968
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.153
GPT teacher head0.443
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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
Citations31
Published2017
Admission routes3
Has abstractno