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Cerebral Amyloid Angiopathy in East and West

2010· review· en· W1489648987 on OpenAlexaff
Yu‐Wei Chen, Ming‐Jen Lee, Eric E. Smith

Bibliographic record

VenueInternational Journal of Stroke · 2010
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Calgary
FundersChinese Academy of Agricultural Sciences
KeywordsCerebral amyloid angiopathyMedicineDementiaNeuroimagingAngiopathyStroke (engine)AsymptomaticAmyloid (mycology)PathologyCerebral arteriesMicroangiopathyCardiologyDiseasePsychiatryDiabetes mellitus

Abstract

fetched live from OpenAlex

Cerebral amyloid angiopathy, a vasculopathy characterised by the deposition of amyloid fibrils in the arteries and arterioles in the cerebral cortex and meninges, has been reported to be associated with intracerebral haemorrhage and cognitive impairment in the elderly. Advances in neuroimaging and validation of the clinical diagnostic criteria aid in making a correct clinical diagnosis. Associations with Alzheimer's disease, asymptomatic microbleeds and white matter changes on neuroimaging have an influence on the clinical treatment for patients with probable cerebral amyloid angiopathy. Reviewing the reports from Asian countries, we found that patients with cerebral amyloid angiopathy have a strong age-related prevalence and a consistent association with dementia, but a weaker correlation with intracerebral haemorrhage, most likely due to a higher incidence of hypertensive intracerebral haemorrhage. Involvement of the occipital lobe arteries by CAA is common in all races and ethnicities, while frontal lobe arteries may be more frequently involved in the East compared to the West. The clinical impact of cerebral amyloid angiopathy on intracerebral haemorrhage and cognitive impairment could be increasingly obvious in Asian countries with ageing populations, especially with improving control of hypertension, the leading cause of intracerebral haemorrhage.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.039
GPT teacher head0.348
Teacher spread0.310 · 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 designOther design
Domainnot available
GenreReview

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

Citations33
Published2010
Admission routes1
Has abstractyes

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