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Record W2543398137 · doi:10.1016/j.jalz.2015.08.114

P4‐284: Vascular risk factors confer domain‐specific deficits in cognitive performance within an elderly russian population

2015· article· en· W2543398137 on OpenAlexaboutno aff
О. А. Макеева, Heather Romero, Valentina V. Markova, Zarui A. Melikyan, Irina Zhukova, L. I. Minaycheva, С. В. Буйкин, Н. Г. Жукова, Yuka Maruyama, Brenda L. Plassman, Allen D. Roses, Kathleen A. Welsh‐Bohmer

Bibliographic record

VenueAlzheimer s & Dementia · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionPopulationDomain (mathematical analysis)PsychologyMedicineNeuroscienceEnvironmental healthMathematics

Abstract

fetched live from OpenAlex

Vascular risk factors have been shown to contribute to risk of cognitive impairment in later life. A large urban elderly population from Tomsk, Russia was studied with respect to age, education, and association between cardiovascular and cerebrovascular conditions and cognitive performance, using the Montreal Cognitive Assessment (MoCA). Volunteers were identified through the local centralized medical care system. All were invited for neurocognitive assessment as a part of a study of cognitive aging, regardless of health history. Detailed information was collected on 2073 individuals, including demographics, medical history, and family history. Arterial hypertension was reported by 82.4% of participants, 42.9% reported hypercholesterolemia, 38.4% were obese (defined as body mass index ≥30), 32.3% had one or more other cardiovascular conditions (defined as history of heart attack, pacemaker, or valve replacement), 28.5% had atrial fibrillation (afib), 19.3% reported type 2 diabetes, and 8.5% survived stroke. The cognitive battery, which included the MoCA, CERAD Word List Learning and Recall, and Trails B, was administrated by trained psychometrists (Hayden et al., 2014). Mean age was 72±5 years (from 56 to 90), 77.1% were female. Age (r=-0.338, p<0.001) and education (r=+0.422, p<0.001) significantly influenced MoCA total score, but male and female subjects performed similarly. A series of multiple regressions were conducted to determine whether vascular disease predicted MoCA scores after controlling for covariates of age and education. Health variables significantly predicted MoCA total scores, F(9, 1837) = 67.80, MoCA percent retention memory scores, F(9,1828) = 11.662, and MoCA executive function scores, F(9, 1837) = 35.33, all models were significant at p<0.001. However, only afib (β=-0.05, p<0.05) and stroke (β=-0.05, p<0.05) individually predicted MoCA total scores, diabetes (β=-0.05, p<0.05) predicted poorer memory performance (determined as percent retention), and afib (β=-0.08, p<0.001) predicted poor executive function. As expected, diabetes, afib and other cardiovascular and cerebrovascular conditions and risk factors were prevalent in this Russian urban elderly population. Vascular risk factors differentially predicted cognitive domains, suggesting differential effects of these risk factors within discrete brain systems. These results indicate that timely treatment and effective control of certain vascular risk factors may help maintain cognition in later life.

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)0.000
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 score0.017
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.282
Teacher spread0.207 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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