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Investigation of cognitive function in elderly hypertensive patients in community of Beijing

2014· article· en· W3030533888 on OpenAlexaboutno aff
Xin Gao, Li Bao, Huiyan Yu, Bin Qin, Fangkun Gao

Bibliographic record

VenueChin J Cardiovasc Med · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionMedicineInternal medicineBeijingBlood pressureRecallCognitive impairmentPsychiatryPsychology

Abstract

fetched live from OpenAlex

Objective To investigate the relationship between hypertension and cognitive function in community-based elderly people aged 60 and over in Beijing. Methods A total of 264 patients with hypertension and 271 cases of patients with normal blood pressure were investigated in this study. The Montreal Cognitive Assessment (MoCA) and Mini-Mental Status Examination (MMSE) were used to determine cognitive change. Results The MMSE scores of total score showed no significant difference (27.98±2.68 vs. 28.37±2.81, t=1.634, P=0.103). The subscores of memory showed significantly decreased in hypertensive group (2.27±0.84 vs. 2.46±0.76, t=2.747, P=0.006). The MoCA scores of total score, subscores of visuospatial and executive, language, delayed recall were significantly decreased in hypertensive group (24.61±4.55 vs. 25.61±4.44, t=2.572, P=0.01; 3.78±1.37 vs. 4.04±1.22, t=2.355, P=0.019; 2.22±0.80 vs. 2.39±0.74, t=2.698, P=0.007; 2.39±1.60 vs. 2.66±1.60, t=1.978, P=0.048, respectively). Conclusions Hypertension has damage on the cognitive function of patients. MoCA is sensitive in clinical assessment of cognitive impairment. Key words: Hypertension; Cognition; Aged

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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.267
Teacher spread0.242 · 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
Published2014
Admission routes1
Has abstractyes

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