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Record W3008718655 · doi:10.1101/2020.01.21.20018101

Comprehensive assessment of <i>PINK1</i> variants in Parkinson’s disease

2020· preprint· en· W3008718655 on OpenAlexaff
Lynne Krohn, Francis P. Grenn, Mary B. Makarious, Jonggeol Jeffrey Kim, Sara Bandrés‐Ciga, Dorien A. Roosen, Ziv Gan‐Or, Mike A. Nalls, Andrew Singleton, Cornelis Blauwendraat

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Environmental Health SciencesNational Institute of Neurological Disorders and StrokeGenentechBiogenGlaxoSmithKlineNational Institute on AgingEli Lilly and CompanyBristol-Myers SquibbMichael J. Fox Foundation for Parkinson's ResearchU.S. Department of Health and Human ServicesNational Institutes of HealthH. Lundbeck A/S
KeywordsPINK1Parkinson's diseaseDiseaseParkinsonismMutationGeneticsLRRK2MedicineParkinBiologyGeneInternal medicine

Abstract

fetched live from OpenAlex

Abstract Multiple genes have been associated with monogenic Parkinson’s disease and Parkinsonism syndromes. Mutations in PINK1 (PARK6) have been shown to result in autosomal recessive early onset Parkinson’s disease. In the past decade, several studies have suggested that carrying a single heterozygous PINK1 mutation is associated with increased risk for Parkinson’s disease. Here we comprehensively assess the role of PINK1 variants in Parkinson’s disease susceptibility using several large datasets totalling 376,558 individuals including: 13,708 Parkinson’s disease cases and 362,850 controls. After combining these data, we did not find evidence to support a role for heterozygous PINK1 mutations as a risk factor for Parkinson’s disease.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.320
Teacher spread0.277 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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