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Record W2307098277 · doi:10.1080/13825585.2016.1161000

A study of mild cognitive impairment in veterans: role of hypertension and other confounding factors

2016· article· en· W2307098277 on OpenAlexaboutno aff
Jie Bai, Ning Zhao, Ying Xiao, Chunhui Yang, Jun Zhong, Yongshun Cai, Yongchao Li, Qin Zhu, Xian Cao, Li Sun, Bing Wang, Keqin Teng, Shifeng Ling, Hailai Ni, Minghui Xie, Jiping Tan, Luning Wang, Xiaomao Sun, Wen-Jun Zhang

Bibliographic record

VenueAging Neuropsychology and Cognition · 2016
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsConfoundingMedicineMontreal Cognitive AssessmentRisk factorDiabetes mellitusInternal medicineCognitive impairmentCognitionDiseaseType 2 diabetesCognitive declinePhysical therapyPsychiatryDementiaEndocrinology

Abstract

fetched live from OpenAlex

INTRODUCTION: Hypertension has shown to be an important risk factor for the decline in cognitive function. Aim of our study is to investigate the presence of cognitive impairment of the elders with hypertension and other confounding factors. METHODS: This study was conducted on 400 veterans who were matched one-to-one with the confounding factors for assessing the presence of mild cognitive impairment using both MMSE and Montreal Cognitive Assessment (MoCA). The 13 related factors of patient data were studied. RESULTS: The prevalence rate of cognitive impairment was 29.25%. Age (OR 2.679, 95%CI 1.663-6.875), sleep impairment (OR 1.117, 95%CI 1.754-7.422), uncontrolled hypertension (OR 1.522, 95%CI 1.968-4.454), type 2 diabetes (OR 2.464, 95%CI 1.232-4.931), and hyperlipidaemia (OR 1.411, 95%CI 1.221-8.988) are the risk factors for the cognitive deterioration, while the protective factors are high level of education (OR 0.032, 95%CI 0.007-0.149) and regular exercise (OR 0.307, 95%CI 0.115-0.818). DISCUSSION: Because some vascular disease risk factors, such as hypertension, can be treated effectively, cognitive decline related to these risk factors, and vascular disease per se, may be prevented or its course modified through more aggressive treatment and improved compliance.

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 score0.095
Threshold uncertainty score0.290

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 communication0.0000.000
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 head0.048
GPT teacher head0.342
Teacher spread0.294 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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