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Record W2980731350 · doi:10.1016/j.jalz.2019.06.4647

O3‐04‐06: COMORBID AMYLOID‐β PATHOLOGY AFFECTS NEUROPSYCHIATRIC, NEUROPSYCHOLOGICAL, AND IMAGING FEATURES IN VASCULAR COGNITIVE DISORDERS

2019· article· en· W2980731350 on OpenAlexaff
Jolien F. Leijenaar, Colin Groot, Carole H. Sudre, David Bergeron, Anna E. Leeuwis, M. Jorge Cardoso, Ferrán Prados, Robert Laforce, Frederik Barkhof, Wiesje M. van der Flier, Philip Scheltens, Niels D. Prins, Rik Ossenkoppele

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsNeuropsychologyMedicineHyperintensityAtrophyCohortInternal medicineDementiaPathologyCognitionPsychiatryDiseaseMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

The relevance of co-morbid amyloid-β (Aβ) pathology in patients with vascular cognitive disorders (VCD) is largely unknown. We, therefore, investigated the impact of co-morbid Aβ pathology on neuropsychiatric and neuropsychological features, white matter hyperintensities (WMH), and gray matter atrophy in a group of well-characterized VCD patients. We retrospectively included 218 patients from the Amsterdam Dementia Cohort with mild or major VCD based on the VASCOG criteria. We used CSF Aβ42 levels to divide patients into four groups: Aβ+ mild VCD (n=84;71±7yrs;52%F), Aβ- mild VCD (n=68;68±9yrs;31%F), Aβ+ major VCD (n=31;69±8yrs;32%F) and Aβ- major VCD (n=35;66±8yrs;26%F). Depressive symptoms were assessed using the Geriatric Depression Score. We measured memory, executive function, attention, visuospatial function and language with a standardized neuropsychological assessment and calculated composite z-scores. WMH volumes were quantified on FLAIR images using a Bayesian Gaussian Mixture Model. To assess gray matter atrophy, we performed voxel-wise analyses on T1-weighted MRI using SPM12. Analyses were stratified by clinical severity (i.e. mild vs major VCD). In mild VCD, Aβ- patients were younger and less often female than Aβ+. Major VCD patients did not differ in age and sex. We found markers of small vessel disease in 96%, and large vessel disease in 15% of all patients. Independent of disease severity, Aβ- patients showed more depressive symptoms than Aβ+. In major VCD, Aβ- patients performed worse on attention (-2.7±0.2vs-2.0±0.2;p=0.02) and executive functioning than Aβ+ patients (-3.0±0.2vs-2.4±0.2;p=0.008). We found no differences on cognition in the mild VCD group. In mild VCD, Aβ- had more WMH than Aβ+ patients. By contrast, in major VCD, Aβ+ was associated with a higher WMH load, predominantly in the occipital and temporal regions (figure-1). We found less gray matter atrophy in the precentral sulcus and temporal pole in Aβ+ mild VCD compared to Aβ- mild VCD. In major VCD, gray matter atrophy patterns did not differ (figure-2).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.010
GPT teacher head0.288
Teacher spread0.278 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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