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Record W4205743391 · doi:10.3233/jpd-212984

Cardiovascular Risk Factors and Phenoconversion to Neurodegenerative Synucleinopathies in Idiopathic REM Sleep Behavior Disorder

2022· letter· en· W4205743391 on OpenAlexaff
Sheida Zolfaghari, Natalia Lewandowski, Amélie Pelletier, Seyed Ali Naeimi, Jean‐François Gagnon, Marina Brillon-Corbeil, Jacques Montplaisir, Ronald B. Postuma

Bibliographic record

VenueJournal of Parkinson s Disease · 2022
Typeletter
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityUniversité de MontréalHôpital du Sacré-Cœur de MontréalMcGill University Health Centre
Fundersnot available
KeywordsSynucleinopathiesDementia with Lewy bodiesMedicineREM sleep behavior disorderDementiaPolysomnographyParkinsonismDiabetes mellitusDiseaseParkinson's diseaseInternal medicineCardiologyAlpha-synucleinEndocrinology

Abstract

fetched live from OpenAlex

Several studies have suggested that atherosclerotic diseases and diabetes may be risk factors for α-synucleinopathies. This prospective cohort study evaluated whether cardiovascular diseases and metabolic risk factors alter the rate or type of phenoconversion from idiopathic/isolated REM sleep behavior disorder (iRBD) to parkinsonism or dementia. Polysomnography-confirmed iRBD patients recruited between 2004 and 2020 were followed annually. Baseline history of cardiovascular disorders, hypertension, hypercholesterolemia, and diabetes were compared among patients who developed outcomes versus those who remained outcome-free. No atherosclerotic risk factors were associated with development of α-synucleinopathies. Patients with hypercholesterolemia were somewhat more likely to develop dementia with Lewy bodies rather than 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)0.001
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
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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