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Record W2747939518 · doi:10.1111/jnc.14157

Cerebral amyloid angiopathy as a cause of neurodegeneration

2017· review· en· W2747939518 on OpenAlexafffund
Eric E. Smith

Bibliographic record

VenueJournal of Neurochemistry · 2017
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHealth Sciences CentreSouth Health CampusUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlzheimer SocietyPfizerFondation Brain CanadaHeart and Stroke Foundation of CanadaUniversity of Calgary
KeywordsCerebral amyloid angiopathyNeurodegenerationDementiaNeuroscienceCognitive declineAtrophyMedicineStroke (engine)DiseasePathologyPsychology

Abstract

fetched live from OpenAlex

Sporadic, age-related cerebral amyloid angiopathy (CAA) is most commonly recognized clinically as a cause of hemorrhagic stroke and transient focal neurological episodes in older persons. But a growing body of research in the last 5 years shows that the pathophysiology of CAA is much more complex than previously believed, leading to many different types of brain injury. CAA has now been linked with brain atrophy in regions remote from those directly affected by intracerebral hematomas, and with risk for progressive cognitive decline in the absence of new hemorrhagic strokes. Therefore, CAA is associated with features - brain atrophy and progressive cognitive decline - that are typically considered hallmarks of neurodegenerative disease. Although CAA is usually accompanied by some degree of Alzheimer's disease pathology, the profiles of cortical thinning and cognitive impairment do not fully overlap with those seen in Alzheimer's disease, suggesting that there are CAA-specific pathways of neurodegeneration. CAA-related brain ischemia may be an important mechanism that leads to brain injury, cortical disconnection, and cognitive impairment. This article is part of the Special Issue "Vascular Dementia".

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.134
GPT teacher head0.425
Teacher spread0.291 · 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 designNot applicable
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

Citations86
Published2017
Admission routes2
Has abstractyes

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