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Record W4408532001 · doi:10.1016/j.gimo.2025.102457

P609: Mainstreaming genetics: Evaluation of a digital application to scale and spread oncologist-initiated genetic testing

2025· article· en· W4408532001 on OpenAlexaff
Daniel Assamad, Marc Clausen, Rita Kodida, Kathleen Bell, Andrea Eisen, Christine Elser, Adena Scheer, Emily Seto, Kevin E. Thorpe, Seema Panchal, Yvonne Bombard

Bibliographic record

VenueGenetics in Medicine Open · 2025
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsHospital for Sick ChildrenSickKids FoundationUniversity Health NetworkUniversity of TorontoSunnybrook Health Science CentreSinai Health SystemHealth Sciences CentreMount Sinai HospitalSt. Michael's Hospital
Fundersnot available
KeywordsScale (ratio)MainstreamingMedical physicsMedical geneticsGenetic testingMedicineGeneticsOncologyComputational biologyInternal medicineComputer scienceBiologyPsychologyGeographyMathematics educationCartographyGeneSpecial education

Abstract

fetched live from OpenAlex

Genetic testing can alter therapy and surgical management for cancer patients and is indicated as a first-line test for many newly diagnosed patients, including breast, ovarian, pancreatic, prostate and colon/GI patients. To reduce pressure on constrained genetics clinics, some cancer centers are mainstreaming genetic testing, which means oncologists, instead of genetics experts, will order genetic testing without traditional pre-test genetic counseling; often using some form of paper-based patient pamphlets or a checklist.

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)0.006
metaresearch head score (Gemma)0.020
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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.057
GPT teacher head0.346
Teacher spread0.289 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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