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Record W575146699 · doi:10.1016/j.jalz.2015.05.014

The effect of white matter hyperintensities on neurodegeneration in mild cognitive impairment

2015· article· en· W575146699 on OpenAlexfundno aff
Giuseppe Tosto, Molly E. Zimmerman, Jamie Hamilton, Owen Carmichael, Adam M. Brickman

Bibliographic record

VenueAlzheimer s & Dementia · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingCanadian Institutes of Health ResearchNational Institutes of HealthAlzheimer's Disease Neuroimaging InitiativeNational Institute of Biomedical Imaging and BioengineeringNorthern California Institute for Research and EducationFoundation for the National Institutes of HealthU.S. Department of Defense
KeywordsHyperintensityNeurodegenerationAtrophyWhite matterEntorhinal cortexPsychologyMagnetic resonance imagingNeuroscienceDementiaCardiologyAlzheimer's diseaseMedicineInternal medicineDiseaseHippocampusRadiology

Abstract

fetched live from OpenAlex

INTRODUCTION: It is unclear whether white matter hyperintensities (WMHs), magnetic resonance imaging markers of small-vessel cerebrovascular disease, promote neurodegeneration and associated clinical decline in Alzheimer's disease (AD), or simply co-occur with recognized pathogenic processes. METHODS: In 169 patients with mild cognitive impairment, followed for 3 years, we examined the association of (1) baseline regional WMH and cerebral spinal fluid-derived t-tau (total tau) with entorhinal cortex atrophy rates, as a marker of AD-related neurodegeneration, and conversion to AD; and (2) baseline regional WMH with change in t-tau level. RESULTS: In participants with low baseline t-tau, higher regional WMH volumes were associated with faster entorhinal cortex atrophy. Higher parietal WMH volume predicted conversion to AD in those with high t-tau. Higher parietal and occipital WMH volumes predicted increasing t-tau. DISCUSSION: WMHs affect AD clinical and pathologic processes both directly and interacting with tau.

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 score0.152
Threshold uncertainty score0.480

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 head0.027
GPT teacher head0.304
Teacher spread0.277 · 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

Citations111
Published2015
Admission routes1
Has abstractyes

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