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Record W2917107887 · doi:10.1093/brain/awz030

Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study

2019· article· en· W2917107887 on OpenAlexafffund
Ronald B. Postuma, Álex Iranzo, Birgit Högl, Bradley F. Boeve, Raffaele Manni, Wolfgang H. Oertel, Isabelle Arnulf, Luigi Ferini‐Strambi, Monica Puligheddu, Elena Antelmi, Valérie Cochen De Cock, Dario Arnaldi, Brit Mollenhauer, Aleksandar Videnović, Karel Šonka, Ki‐Young Jung, Dieter Kunz, Yves Dauvilliers, Federica Provini, Simon J.G. Lewis, Jitka Bušková, Milena Pavlova, Anna Heidbreder, Jacques Montplaisir, Joan Santamaría, Thomas R. Barber, Ambra Stefani, Erik K. St. Louis, Michele Terzaghi, Annette Janzen, Smandra Leu-Semenescu, Giuseppe Plazzi, Flavio Nobili, F. Sixel-Doering, Petr Dušek, Frédérik Bes, Pietro Cortelli, Kaylena A. Ehgoetz Martens, Jean‐François Gagnon, Carles Gaig, Marco Zucconi, Claudia Trenkwalder, Ziv Gan‐Or, Christine Lo, Michal Rolinski, Philipp Mahlknecht, Evi Holzknecht, Angel R Boeve, Luke Teigen, Gianpaolo Toscano, Geert Mayer, Silvia Morbelli, Benjamin Dawson, Amélie Pelletier

Bibliographic record

VenueBrain · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsMontreal Neurological Institute and HospitalUniversité du Québec à MontréalMcGill UniversityCanadian Sleep & Circadian NetworkHôpital du Sacré-Cœur de MontréalMontreal General Hospital
FundersCanadian Institutes of Health ResearchNational Institute for Health and Care Research
KeywordsParkinsonismDementiaREM sleep behavior disorderPsychiatrySleep (system call)PsychologyMedicineClinical psychologyPolysomnographyElectroencephalographyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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

Citations1,097
Published2019
Admission routes2
Has abstractyes

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