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Evaluation of the interaction between LRRK2 and PARK16 loci in determining risk of Parkinson's disease: analysis of a large multicenter study

2016· article· en· W2527872709 on OpenAlexafffund
Lisa Wang, Michael G. Heckman, Jan Aasly, Grazia Annesi, Maria Bozi, Sun Ju Chung, Carl E Clarke, David Crosiers, Gertrud Eckstein, Gaëtan Garraux, Georgios M. Hadjigeorgiou, Nobutaka Hattori, Beom S. Jeon, Yun Joong Kim, Masato Kubo, Suzanne Lesage, Juei Jueng Lin, Timothy Lynch, Peter Lichtner, George D. Mellick, Vincent Mok, Karen Morrison, Aldo Quattrone, Wataru Satake, Peter A. Silburn, Leonidas Stefanis, Joanne Stockton, Eng King Tan, Tatsushi Toda, Alexis Brice, Christine Van Broeckhoven, Ryan J. Uitti, Karin Wirdefeldt, Zbigniew K. Wszołek, Georgia Xiromerisiou, Demetrius M. Maraganore, Thomas Gasser, Rejko Krüger, Matthew J. Farrer, Owen A. Ross, Manu Sharma

Bibliographic record

VenueNeurobiology of Aging · 2016
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Environmental Health SciencesDeutsches Zentrum für Neurodegenerative ErkrankungenArizona Biomedical Research CommissionNational Institute of Neurological Disorders and StrokeNational Institute on AgingNational Medical Research CouncilVlaamse regeringDeutsche ForschungsgemeinschaftCenter for Individualized Medicine, Mayo ClinicSvenska LäkaresällskapetSeoul National University HospitalUniversiteit AntwerpenH. Lundbeck A/SFondation de FranceVetenskapsrådetSeoul National UniversityAssociation France ParkinsonSvenska Sällskapet för Medicinsk ForskningHermann und Lilly Schilling-Stiftung für Medizinische ForschungAgence Nationale de la RechercheBiogenFritz Thyssen StiftungSinyang Cultural FoundationKarolinska InstitutetUniversity of ThessalyGriffith UniversityMinistero della SaluteInstitut National de la Santé et de la Recherche MédicaleAustralian GovernmentNational Institutes of HealthVolkswagen FoundationBelgian Federal Science Policy OfficeIpsenFonds Wetenschappelijk OnderzoekJapan Agency for Medical Research and DevelopmentKorea Science and Engineering FoundationParkinsonfondenFondation pour la Recherche sur AlzheimerDuke-NUS Medical SchoolParkinson VerenigingValeant Pharmaceuticals InternationalJapan Society for the Promotion of ScienceGlaxoSmithKlineParkinson Study GroupBundesministerium für Bildung und ForschungMedical Research CouncilTeva Pharmaceutical IndustriesEli Lilly and Company
KeywordsLRRK2Parkinson's diseaseDiseaseRetromerMedicineGeneticsBioinformaticsNeuroscienceBiologyInternal medicineEndosome

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)0.006
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.331
Teacher spread0.300 · 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

Citations8
Published2016
Admission routes2
Has abstractno

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