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Record W4406995753 · doi:10.1161/str.56.suppl_1.tp33

Abstract TP33: Microhemorrhages and Cortical Superficial Siderosis in Cognitively Impaired Patients: Prevalence and Risk Factors

2025· article· en· W4406995753 on OpenAlexaffabout
Hessah A. Alotibi, Juan‐Camilo Vargas‐González, Tartaglia M Carmela

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSuperficial siderosisCerebral amyloid angiopathySiderosisStroke (engine)Risk factorPathologyDementiaDisease

Abstract

fetched live from OpenAlex

(1) Background: Cerebral microbleeds (MBs) and cortical superficial siderosis (cSS) can be seen in a multitude of etiologies. The risk factors for cerebral MB and cSS and their relationship with cognitive decline are not well known. (2) Objective: This study aimed to explore the risk factors for cerebral MBs and cSS and to examine their impact on cognitive function in a cohort of cognitively impaired patients. (3) Methods: We conducted a case-control study involving 415 patients who underwent a brain magnetic resonance imaging (MRI) with a dementia protocol and cognitive assessments at the University Health Network, Mount Sinai Hospital, and Women's College Hospital in Toronto from 2014 to 2022. Cognition was assessed using the Mini-Mental State Examination (MMSE) and/ or the Montreal Cognitive Assessment (MoCA) The case group (n=137) included patients with cerebral MBs and/or cSS, while the control group (n=278) was comprised of randomly selected patients for cognitive impairment and underwent the same MRI protocol. We also evaluated the location of the MB including if deep, lobar or mixed MB. White matter hyperintensities (WMH) were graded using the Fazekas scale, wherein severity is assessed from 0 to 3. We performed Multivariate logistic regression to identify risk factors associated with cerebral MBs and cSS. (4) Results: Patients with MBs and/or cSS were older and had a higher prevalence of hypertension compared to the control group. In the multivariate analysis, age (OR = 1.05, 95% CI: 1.02-1.08, p = 0.002), hypertension (OR = 2.67, 95% CI: 1.75-4.08, p < 0.001), and higher Fazekas scores (OR = 1.58, 95% CI: 1.22-2.06, p < 0.001) were associated with cerebral MBs and cSS. Cognitive scores were significantly lower in the group with MB and cSS (MMSE: 22.3 vs. 26.1; MoCA: 19.7 vs. 24.8, both p < 0.001). Regression analysis showed that MB location did not significantly affect cognitive scores. The estimates for deep (0.45, 95% CI: -0.38 to 1.27; p < 0.286), lobar (-0.17, 95% CI: -0.55 to 0.21; p < 0.379), and mixed (-0.12, 95% CI: -0.47 to 0.24; p < 0.522) (5) Conclusions: Age, hypertension, and small vessel disease as measured by Fazekas scale are significant risk factors for cerebral MBs and cSS. Cognitive impairment was more severe in those with MBs and cSS and not related to their location. It is essential to manage vascular risk factors, such as hypertension,as this is ssociated with MBs and cSS , which are associated with worse cognitive impairment

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)0.000
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.273
Teacher spread0.261 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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