Cognitive Impairment in the Elderly: a Survey and Analysis of Influencing Factors
Bibliographic record
Abstract
Background With the intensifying trend of population aging, cognitive impairment has become one of China's significant public health challenges. The impact of lifestyle on cognitive impairment warrants further exploration. Objective This study aims to ascertain the prevalence of cognitive impairment among the elderly in the Baoshan District of Shanghai and analyze how educational level, economic status, lifestyle, and comorbidities affect cognitive impairment, providing a scientific basis for early prevention and control. Methods From July 2020 to August 2020, a stratified random sampling method was employed to survey 374 residents aged 65 and older in the Dachang Community of Baoshan District, using the Montreal Cognitive Assessment Basic Scale (MoCA-B) for cognitive evaluation. Multivariate Logistic regression analysis was used to explore the factors influencing cognitive impairment among the elderly. Results A total of 374 valid questionnaires were retrieved, with a response rate of 100.0%. The prevalence of cognitive impairment among the suburban elderly population over the age of 65 in Shanghai was 37.7% (141/374). The multivariate Logistic regression analysis indicated that socializing and chatting (OR=0.574, 95%CI=0.350-0.941) were protective factors against cognitive impairment (P<0.05), while aging (OR=1.568, 95%CI=1.207-2.307), living alone (OR=3.569, 95%CI=1.079-11.807), and daily sedentary time of ≥3 hours (OR=1.944, 95%CI=1.091-3.462) were risk factors (P<0.05) . Conclusion Over one-third of the elderly in the suburban areas of Shanghai suffer from cognitive impairment; advanced age, living alone, and prolonged daily sedentary behavior are significant risk factors that should be closely monitored.
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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) | 0.000 | 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".