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Nocturnal Limb Movements Are Correlated with Cerebral White Matter Hyperintensities (P05.001)

2012· article· en· W2316093669 on OpenAlexaffabout
Mark I. Boulos, Jacqueline A. Pettersen, D. Jewell, Sandra E. Black, Brian C. Murray

Bibliographic record

VenueNeurology · 2012
Typearticle
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British ColumbiaHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsHyperintensityNocturnalMedicinePhysical medicine and rehabilitationCardiologyInternal medicineMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Objective: To explore the association of nocturnal limb movements (LMs) and sleep quality with cerebral white matter hyperintensities (WMH). Background Nocturnal LMs are associated with transient but significant increases in night-time blood pressure and autonomic hyperactivity; emerging evidence suggests a link with vascular disease. While obstructive sleep apnea is linked with WMH, the relationship between nocturnal LMs and WMH remains to be clarified. Design/Methods: Patients evaluated in a tertiary care behavioral neurology clinic were assessed with polysomnography for various sleep problems. Polysomnography was scored according to criteria from the American Academy of Sleep Medicine. WMH were rated using the Age Related White Matter Changes Score (ARWMC) from FLAIR MRI. Polysomnographic (transformed where necessary) and ARWMC data were compared using Pearson correlations. Results: Forty-five participants were assessed (69% male, mean age 64 years) and vascular risk factors were as follows: hypertension (27%), hyperlipidemia (18%) and diabetes (9%). Prior cerebrovascular disease (7%), obstructive sleep apnea (49%) and restless legs syndrome (33%) were also present. The mean ARWMC score was 3.84 (range 0-25, standard deviation 4.73). When controlling for hypertension, the total ARWMC score was correlated with total LMs per hour of sleep (r=0.66, p=0.01). There were no differences in the ARWMC score between hemispheres, but more LMs were noted on the left side (66.3 vs. 24.1, p Conclusions: LM counts strongly correlated with the presence of WMH. In addition, sleep efficiency was negatively correlated with WMH. In keeping with developing evidence, our findings suggest that nocturnal LMs associated with poor quality sleep may contribute to episodes of nocturnal hypertension, which have been implicated in the development of WMH, even after controlling for the presence of daytime hypertension. Supported by: Dr. Mark Boulos is supported by a Focus on Stroke Research Fellowship, which is funded by the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Canada, and the Canadian Stroke Network. Disclosure: Dr. Boulos has nothing to disclose. Dr. Pettersen has nothing to disclose. Dr. Jewell has nothing to disclose. Dr. Black has received personal compensation for activities with Novartis Pharmaceuticals, Pfizer, GlaxoSmithKline, Roche Pharmaceuticals, and Bristol-Myers Squibb. Dr. Black has received research support from Novartis Pharmaceuticals, Pfizer, Roche, and GlaxoSmithKline. Dr. Murray has received personal compensation for activities with Pfizer and Valeant as a consultant.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

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
Published2012
Admission routes2
Has abstractyes

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