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Record W4410897339 · doi:10.1038/s41431-025-01871-4

The development and usability of ‘The Genetics Navigator’: a digital solution for adult and paediatric clinical genetics services

2025· article· en· W4410897339 on OpenAlexafffund
Saumeh Saeedi, Daena Hirijkaka, Marc Clausen, Stephanie Luca, Emma Reble, Rita Kodida, Daniel Assamad, Lauren Chad, Gregory Costain, Hanna Faghfoury, Josh Silver, Serena Shastri-Estrada, Maureen Smith, Robin Z. Hayeems, Yvonne Bombard, Melyssa Aronson, François P. Bernier, Michael Brudno, June Carroll, Ronald D. Cohn, Irfan Dhalla, Jan M. Friedman, S. Hewson, Trevor Jamieson, Rebekah Jobling, Anne‐Marie Laberge, Jordan Lerner‐Ellis, Eriskay Liston, Muhammad Mamdani, Christian R. Marshall, Matthew Osmond, Quỳnh Phạm, Frank Rudzicz, Emily Seto, Cheryl Shuman, Kevin E. Thorpe, Wendy J. Ungar

Bibliographic record

VenueEuropean Journal of Human Genetics · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPublic Health OntarioVector InstituteCentre for Global Health ResearchChildren's Hospital of Eastern OntarioCentre Hospitalier Universitaire Sainte-JustineLunenfeld-Tanenbaum Research InstituteAlberta Children's HospitalToronto Rehabilitation InstituteSinai Health SystemUniversity Health NetworkSickKids FoundationUniversity of TorontoUniversity of OttawaInstitute for Clinical Evaluative SciencesUniversity of British ColumbiaHospital for Sick ChildrenSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedical geneticsUsabilityHuman geneticsGeneticsStatistical geneticsBiologyComputational biologyComputer scienceGenomicsHuman–computer interactionGenome

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)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0260.008

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 head0.017
GPT teacher head0.303
Teacher spread0.286 · 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 designBench or experimental
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

Citations2
Published2025
Admission routes2
Has abstractno

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