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Record W3092507974 · doi:10.1002/mds.28326

Restless Leg Syndrome and Objectively‐Measured Atherosclerosis in the Canadian Longitudinal Study on Aging

2020· article· en· W3092507974 on OpenAlexafffundabout
Sheida Zolfaghari, Kaberi Dasgupta, Ronald B. Postuma

Bibliographic record

VenueMovement Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsCentre for Advancing Health OutcomesHôpital du Sacré-Cœur de MontréalMcGill University Health CentreMcGill University
FundersCanadian Institutes of Health Research
KeywordsRestless legs syndromeMedicineDepression (economics)AnxietyIntima-media thicknessInternal medicineLongitudinal studyCardiologyCross-sectional studyCarotid arteriesPhysical therapyPsychiatryPathologyNeurology

Abstract

fetched live from OpenAlex

BACKGROUND: Previous studies suggest associations between restless leg syndrome (RLS) and atherosclerosis, but these have primarily been based upon subjective atherosclerotic measures. OBJECTIVE: We evaluated associations between RLS and an objective indicator of atherosclerosis, namely carotid intima-media thickness (cIMT). METHODS: In this cross-sectional study among 30,097 Canadian Longitudinal Study on Aging (CLSA) participants, we used a four-item questionnaire to screen for probable-RLS. cIMT was measured at the bifurcation of the common carotid artery. Associations were tested with linear regression adjusting for age, sex, ferritin, pulmonary disease, smoking, alcohol, physical activity, anxiety, depression, and other sleep diagnoses. RESULTS: Among 26,304 included participants, 2047 (7.8%) had probable-RLS. Mean cIMT was higher (0.755 ± 0.17 vs 0.736 ± 0.17, P < 0.001) in those with RLS, even after excluding those without prior atherosclerotic diseases (0.740 ± 0.17 vs 0.723 ± 0.16, P = 0.016). CONCLUSION: RLS is associated with objective measures of atherosclerosis. © 2020 International Parkinson and Movement Disorder Society.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.198
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.132
GPT teacher head0.346
Teacher spread0.215 · 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 teacher head, 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

Citations9
Published2020
Admission routes3
Has abstractyes

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