MétaCan
Menu
Back to cohort
Record W2234467592 · doi:10.1161/strokeaha.115.011608

Sleep Fragmentation, Cerebral Arteriolosclerosis, and Brain Infarct Pathology in Community-Dwelling Older People

2016· article· en· W2234467592 on OpenAlexafffund
Andrew Lim, Lei Yu, Julie A. Schneider, David A. Bennett, Aron S. Buchman

Bibliographic record

VenueStroke · 2016
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Neurological Disorders and StrokeNational Institute on AgingCanadian Institutes of Health Research
KeywordsArteriolosclerosisMedicineOdds ratioCerebral amyloid angiopathyAutopsyConfidence intervalInternal medicineCardiologyPathologyDementiaDisease

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Although several forms of sleep disruption are associated with stroke, few studies have examined the relationship between sleep and histopathologic measures of cerebrovascular disease. We tested the hypothesis that greater sleep fragmentation is associated with a higher burden of cerebral vessel and infarct pathology at autopsy. METHODS: We used ordinal logistic regression models to relate sleep fragmentation measured by actigraphy to the severity of arteriolosclerosis, atherosclerosis, and cerebral amyloid angiopathy, and the number of macroscopic and microscopic infarcts assessed by structured brain autopsy in 315 participants from the Rush Memory and Aging Project. RESULTS: Greater sleep fragmentation was associated with more severe arteriolosclerosis (odds ratio, 1.27; 95% confidence interval, 1.02-1.59; P=0.03 per 1 SD greater sleep fragmentation) and more subcortical macroscopic infarcts (odds ratio, 1.31; 95% confidence interval, 1.01-1.68; P=0.04). These associations were independent of established cardiovascular risk factors and diseases, and several medical comorbidities. CONCLUSIONS: Sleep fragmentation is associated with arteriolosclerosis and subcortical infarcts in older adults.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.953

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 integrity0.0000.000
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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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

Citations58
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueStrokeSame topicSleep and related disordersFrench-language works237,207