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Record W4392592405 · doi:10.1016/j.gimo.2024.101780

P866: Exploring the impact of secondary findings in a cohort of patients and families receiving genome-wide sequencing

2024· article· en· W4392592405 on OpenAlexaff
Katharine Fooks, Lydia Vermeer, Elise Poole, Stephanie Luca, Riyana Babul‐Hirji, Lauren Chad, David Chitayat, Michael P. Mackley, M. Schwartz, Wendy J. Ungar, Robin Z. Hayeems, Secondary Findings Study Team, Joyce Yan, Abigail Hansen, Viji Venkataramanan, Daniel Assamad, Christian R. Marshall, Meredith Gillespie, Anna Szuto, Caitlin Chisholm, James Stavropoulos, Lijia Huang, Olga Jarinova, Lynette Lau, Whiwon Lee, Lauren Badalato, Tuğçe B. Balcı, Cara Inglese, Virginie Beauséjour Ladouceur, Chantal F. Morel, Julie Richer, Mark A. Tarnopolsky, Anita Villani, Laura Zahavich, Olivia Moran, Sarah L. Sawyer, Roberto Mendoza‐Londono, Martin J. Somerville, Kym M. Boycott

Bibliographic record

VenueGenetics in Medicine Open · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of OttawaMcMaster UniversityLondon Health Sciences CentreQueen's UniversityChildren's Hospital of Eastern OntarioTed Rogers Centre for Heart ResearchUniversity Health NetworkSickKids FoundationUniversity of TorontoKingston Health Sciences CentreHospital for Sick Children
Fundersnot available
KeywordsCohortGenomeGeneticsMedicineBiologyGeneInternal medicine

Abstract

fetched live from OpenAlex

Secondary findings (SF) are defined as genetic test results that are actively sought but unrelated to the primary indication for testing. Approximately 1-4% of individuals having genome-wide sequencing (GWS) receive a medically actionable SF. The American College of Medical Genetics and Genomics currently recommends that 81 genes be analyzed for SF when an individual receives clinical GWS. However, this practice is not universal, and approaches to secondary findings vary across sequencing programs and jurisdictions.

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 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)0.001
metaresearch head score (Gemma)0.008
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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