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Record W2073161926 · doi:10.1161/strokeaha.108.537704

Understanding White Matter Disease

2008· article· en· W2073161926 on OpenAlexaff
Sandra E. Black, Fuqiang Gao, Juan M. Bilbao

Bibliographic record

VenueStroke · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsHeart and Stroke Foundation
Fundersnot available
KeywordsMedicineAstrocytosisLeukoaraiosisDementiaCerebral amyloid angiopathyPathologyWhite matterStroke (engine)HyperintensityDiffusion MRINeuroimagingVascular dementiaMagnetic resonance imagingNeuroscienceDiseaseRadiologyImmunohistochemistryPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Most strokes are covert and observed incidentally on brain scans, but their presence increases risk of overt stroke and dementia. Amyloid angiopathy, associated with Alzheimer Disease (AD) causes stroke, and when even small strokes coexist with AD, they lower the threshold for dementia. Diffuse ischemic white matter disease impairs executive functioning, information processing speed, and gait. Neuroimaging techniques, such as tissue segmentation, Diffusion Tensor Imaging, MR Spectroscopy, functional MRI and amyloid PET, probe microstructural integrity, molecular biology, and activation patterns, providing new insights into brain-behavior relationships. MR-pathological studies of periventricular hyperintensity (leukoaraiosis) in aging and dementia reveal arteriolar tortuosity, reduced vessel density, and occlusive venous collagenosis which causes venous insufficiency and vasogenic edema. Activated microglia, oligodendroglial apoptosis, clasmatodendritic astrocytosis, and upregulated hypoxia-markers are seen on immunohistochemistry. Further research is needed to understand and treat this chronic subcortical vascular disease, which is epidemic in our aging population.

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)0.001
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.058
GPT teacher head0.261
Teacher spread0.203 · 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

Citations382
Published2008
Admission routes1
Has abstractyes

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