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Record W3083572637 · doi:10.1007/s00439-020-02220-9

Beyond medically actionable results: an analytical pipeline for decreasing the burden of returning all clinically significant secondary findings

2020· article· en· W3083572637 on OpenAlexafffund
Emma Reble, Mariana Salazar, Kathleen-Rose Zakoor, Sam Khalouei, Marc Clausen, Rita Kodida, Salma Shickh, Chloe Mighton, Iris Cohn, Kasmintan A. Schrader, Raymond H. Kim, Jordan Lerner‐Ellis, Yvonne Bombard

Bibliographic record

VenueHuman Genetics · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity Health NetworkUniversity of TorontoLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalHospital for Sick ChildrenSinai Health SystemSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsExome sequencingExomeBiologyPipeline (software)Computational biologyPrioritizationPharmacogenomicsDiseaseGeneGeneticsBioinformaticsMedicineComputer scienceInternal medicineMutationEngineering

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)0.035
metaresearch head score (Gemma)0.143
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.035
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.143
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0090.004
Science and technology studies0.0020.001
Scholarly communication0.0080.005
Open science0.0030.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0200.009

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 head0.039
GPT teacher head0.319
Teacher spread0.280 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations20
Published2020
Admission routes2
Has abstractno

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