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Record W2324454318 · doi:10.3389/fnagi.2016.00062

Dynamic Progression of White Matter Hyperintensities in Alzheimer’s Disease and Normal Aging: Results from the Sunnybrook Dementia Study

2016· article· en· W2324454318 on OpenAlexafffund
Joel Ramirez, Alicia A. McNeely, Courtney Berezuk, Fuqiang Gao, Sandra E. Black

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2016
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHealth Sciences CentreUniversity of TorontoThe Scarborough HospitalHeart and Stroke FoundationSunnybrook Health Science Centre
FundersLinda C. Campbell FoundationUniversity of TorontoCanadian Institutes of Health ResearchSunnybrook Research InstituteAlzheimer SocietyCanadian Vascular NetworkHeart and Stroke Foundation of Canada
KeywordsHyperintensityDementiaBrain agingMedicineDiseaseAlzheimer's diseaseWhite matterNeurosciencePsychologyAudiologyInternal medicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Although white matter hyperintensities (WMH), markers of cerebral small vessel disease (SVD), are believed to generally increase over time, some studies have shown sharp decreases after therapeutic intervention, suggesting that WMH progression may be more dynamic than previously thought. Our primary goal was to examine dynamic progression of WMH in a real-world sample of Alzheimer's disease (AD) patients and normal elderly (NC), with varying degrees of SVD. WMH volumes from serial magnetic resonance imaging (MRI; mean = 1.8 years) were measured from NC (n = 44) and AD patients (n = 113) with high and low SVD burden. Dynamic progression for each individual was measured using spatial overlap images to assess shrinkage, growth, and stable WMH volumes. Significant group differences were found for shrinkage (p < 0.001), growth (p < 0.001) and stable (p < 0.001) WMH, where the AD high SVD group showed the largest changes relative to low SVD and NC. Our results suggest spatial progression measured at the individual patient level may be more sensitive to the dynamic nature of WMH.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.301
Teacher spread0.282 · 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

Citations57
Published2016
Admission routes2
Has abstractyes

Explore more

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