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NPC1 variants are not associated with Parkinson’s disease, REM-sleep behavior disorder or dementia with Lewy bodies in European cohorts

2023· article· en· W4323349730 on OpenAlexafffund
Emma N. Somerville, Lynne Krohn, Eric Yu, Uladzislau Rudakou, Konstantin Senkevich, Jennifer A. Ruskey, Farnaz Asayesh, Jamil Ahmad, Dan Spiegelman, Yves Dauvilliers, Isabelle Arnulf, Jacques Montplaisir, Jean‐François Gagnon, Alex Désautels, Abubaker Ibrahim, Ambra Stefani, Birgit Högl, Gian Luigi Gigli, Mariarosaria Valente, Francesco Janes, Andrea Bernardini, Petr Dušek, Karel Šonka, David Kemlink, Giuseppe Plazzi, Elena Antelmi, Francesco Biscarini, Brit Mollenhauer, Claudia Trenkwalder, Friederike Sixel‐Döring, Michela Figorilli, Monica Puligheddu, Valérie Cochen De Cock, Wolfgang H. Oertel, Annette Janzen, Luigi Ferini‐Strambi, Anna Heibreder, Christelle Monaca, Beatriz Abril, Femke Dijkstra, Mineke Viaene, Bradley F. Boeve, Ronald B. Postuma, Guy A. Rouleau, Ziv Gan‐Or

Bibliographic record

VenueNeurobiology of Aging · 2023
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalHôpital du Sacré-Cœur de MontréalMcGill UniversityCanadian Sleep & Circadian NetworkMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchParkinson's UK
KeywordsSynucleinopathiesDementia with Lewy bodiesDementiaAlpha-synucleinNPC1Parkinson's diseaseREM sleep behavior disorderDiseaseMedicineBiologyPathologyGeneticsReceptor

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)0.001
metaresearch head score (Gemma)0.004
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.030
GPT teacher head0.282
Teacher spread0.252 · 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
Published2023
Admission routes2
Has abstractno

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