Analysis of the Prevalence of Mild Cognitive Impairment and its Influencing Factors in the Elderly Population in Huzhou City
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".