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Record W2758546793 · doi:10.1016/j.jalz.2017.07.755

Concomitant vascular and neurodegenerative pathologies double the risk of dementia

2017· article· en· W2758546793 on OpenAlexaff
Mahmoud Reza Azarpazhooh, Abolfazl Avan, Lauren E. Cipriano, David G. Muñoz, Luciano A. Sposato, Vladimir Hachinski

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHeart and Stroke FoundationUniversity of TorontoSt. Michael's HospitalWestern University
FundersFerdowsi University of Mashhad
KeywordsDementiaVascular dementiaMedicineConcomitantPathologyPopulationInternal medicineDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: The relative contributions of vascular and degenerative pathology to dementia are unknown. We aim to quantify the proportion of dementia explained by potentially preventable vascular lesions. METHODS: We systematically searched for population-based cohorts before February 2017 reporting clinicopathological data for individuals with and without dementia. We calculated the summary proportion and absolute risk of dementia comparing subjects with and without the pathology. RESULTS: We identified 10 studies comprising 2856 subjects. Vascular-type pathology and mixed pathology are respectively two and three times more likely in demented patients. The summary proportion of dementia is 77%-86% in subjects with mixed degenerative and vascular pathology and 45% in subjects with pure Alzheimer-type pathology. DISCUSSION: Patients with mixed pathologies have nearly twice the incremental risk of dementia compared with patients with only Alzheimer-type lesions. Consequently, many cases of dementia could be prevented or delayed by targeting the vascular component.

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 score0.096
Threshold uncertainty score0.545

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 studies0.0010.001
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 head0.041
GPT teacher head0.317
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

Citations183
Published2017
Admission routes1
Has abstractyes

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