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Record W4322623258 · doi:10.1016/j.ymgme.2023.107454

THE CLINGEN LYSOSOMAL DISEASES GENE CURATION EXPERT PANEL: APPLYING A STANDARDIZED CURATION FRAMEWORK TO ASSESS THE CLINICAL VALIDITY OF GENES FOR LYSOSOMAL DISEASE

2023· article· en· W4322623258 on OpenAlexaff
Rong Mao, Emily Groopman, Raquel Fernández, Shruthi Mohan, Amber Stafford, Heather Baudet, Meredith Weaver, L. Clarke, Christina Hung, Deeksha Bali, Filippo Pinto e Vairo, Lemuel Racacho, Tatiana Yuzyuk, William Craigen, Jennifer Goldstein

Bibliographic record

VenueMolecular Genetics and Metabolism · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatological diseases and infestations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineOdds ratioConfidence intervalLogistic regressionEnvironmental healthCross-sectional studyVeterinary medicineInternal medicinePathology

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)0.162
metaresearch head score (Gemma)0.180
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.162
Threshold uncertainty score0.858

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1620.180
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0100.005
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0050.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.002

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 head0.130
GPT teacher head0.409
Teacher spread0.279 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractno

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