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Record W4410135738 · doi:10.62641/aep.v53i3.1722

Analysis of the Prevalence of Mild Cognitive Impairment and its Influencing Factors in the Elderly Population in Huzhou City

2025· article· en· W4410135738 on OpenAlexaboutno aff
Weiliang He, Zheli Chen, Liang Xu, Fei Fang, Xiao Zu, Xilong Jin, Jing Chen

Bibliographic record

VenueActas Españolas de Psiquiatría · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDementiaDepression (economics)Geriatric Depression ScaleMontreal Cognitive AssessmentUnivariate analysisCognitive impairmentGerontologyPopulationCognitionActivities of daily livingElderly peopleDiseasePhysical therapyInternal medicineMultivariate analysisPsychiatryDepressive symptomsEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Mild cognitive impairment (MCI) is a critical stage in the development of Alzheimer's disease, and early intervention in patients during this stage may reverse or delay their disease progression. As one of the regions with severe aging in China, it is necessary to understand the prevalence of MCI in Huzhou and adopt effective intervention measures. The study was aimed to investigate the prevalence rate and influencing factors of MCI in the elderly population in Huzhou city. METHODS: A cross-sectional study was conducted involving 800 elderly residents of Huzhou city. The Montreal Cognitive Assessment (MoCA) and the activity of daily living (ADL) were used to assess the occurrence of MCI in the elderly. The influencing factors of MCI were investigated by univariate analysis and multi-factor analysis. RESULTS: A total of 800 questionnaires were sent out in this survey, and 778 were effectively collected, with an effective recovery rate of 97.25%. Among 778 elderly people in Huzhou city, 668 had normal cognitive function, 82 had MCI, and 28 had dementia, the prevalence rate of MCI was 10.54% (82/778). According to the presence or absence of MCI, the patients were divided into an MCI group (n = 82) and a non-MCI group (n = 668). Female (p = 0.026), high age (p = 0.009), low Community Screening Instrument for Dementia (CSI-D) score (p = 0.007), high Dementia Screening Questionnaire (AD8) score (p < 0.001), high Patient Health Questionnaire Depression Scale (PHQ-9) score (p = 0.037) were all risk factors for MCI in the urban elderly population of Huzhou City. CONCLUSION: The prevalence of MCI in the elderly population in Huzhou City is high, and its occurrence is closely related to many factors. It is necessary to increase attention to the high-risk population of MCI and implement targeted intervention measures to improve their cognitive function and improve the quality of life of the elderly population.

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)0.001
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 score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

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

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 head0.019
GPT teacher head0.333
Teacher spread0.314 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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