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Record W4402459585 · doi:10.1002/mds.30006

Consensus Guidance for Genetic Counseling in <scp><i>GBA1</i></scp> Variants: A Focus on Parkinson's Disease

2024· review· en· W4402459585 on OpenAlexaff
Sophia R.L. Vieira, Roxana Mezabrovschi, Marco Toffoli, Sara Lucas Del Pozo, Elisa Menozzi, Stephen Mullin, Selen Yalkic, Naomi Limbachiya, Sofia Koletsi, Nadine Loefflad, Grisel Lopez, Ziv Gan‐Or, Roy N. Alcalay, Ellen Sidransky, Anthony H.V. Schapira

Bibliographic record

VenueMovement Disorders · 2024
Typereview
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Human Genome Research InstituteNational Institutes of HealthIdorsia PharmaceuticalsUniversity College LondonServierParkinson's FoundationJazz PharmaceuticalsMedical Research CouncilBiogenAligning Science Across Parkinson’sU.S. Department of DefenseSanofiMichael J. Fox Foundation for Parkinson's Research
KeywordsGlucocerebrosidaseParkinson's diseaseGenetic counselingDiseaseMedicinePsychiatryGerontologyGeneticsBiologyPathology

Abstract

fetched live from OpenAlex

Glucocerebrosidase (GBA1) variants constitute numerically the most common known genetic risk factor for Parkinson's disease (PD) and are distributed worldwide. Access to GBA1 genotyping varies across the world and even regionally within countries. Guidelines for GBA1 variant counseling are evolving. We review the current knowledge of the link between GBA1 and PD, and discuss the practicalities of GBA1 testing. Lastly, we provide a consensus for an approach to counseling people with GBA1 variants, notably the communication of PD risk. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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)0.005
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0110.008

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 head0.035
GPT teacher head0.338
Teacher spread0.303 · 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
GenreReview

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

Citations22
Published2024
Admission routes1
Has abstractyes

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