[Cognition function and its influencing factors among people aged 55 and above in 4 provinces of China from 2018 to 2019].
Bibliographic record
Abstract
OBJECTIVE: To analyze the current situation of cognition function of people aged 55 and above in 4 provinces of China, and to explore its influencing factors of demographic characteristics. METHODS: Using the baseline data of the "Community-based Cohort Study on Nervous System Diseases", middle-aged and older populations aged ≥55 years with completed data on demographic and economic factors and the cognitive function scale were selected as study subjects. A total of 5103 subjects were included in the study(male 2294, female 2809, 55-64 years old 1875, 65-74 years old 2197, 75-94 years old 1031). Multi-stage stratified cluster random sampling was adopted, and survey subjects were selected from a total of 32 communities in Hebei, Zhejiang, Shaanxi and Hunan provinces. The baseline data obtained from a face-to-face questionnaire survey was entered using electronic tablets on the spot. Montreal cognitive assessment(MoCA) and activities of daily living scale(ADL) were used to determine mild cognitive impairment(MCI) and its subtypes. Multiple linear regression and multiple Logistic regression model were used to analyze the influencing factors of cognitive function in populations. RESULTS: Among middle-aged and elderly Chinese populations, the score of overall cognitive function and its sub-domains were 21. 79±6. 17, 11. 20±4. 18(memory), 8. 81±3. 31(execution), 5. 33±1. 76(visual-spatial ability), 4. 53±1. 40(language), 13. 32±3. 98(attention) and 5. 54±0. 95(orientation). The prevalence of MCI and its subtypes were 35. 86%, 4. 57%(amnestic MCI single domain, aMCI-SD), 3. 64%(nonamnestic MCI single domain, naMCI-SD), 6. 68%(amnestic MCI multiple domains, aMCI-MD) and 3. 94%(nonamnestic MCI multiple domains, naMCI-MD). Subjects aged ≥55 years, living in rural areas, or with per capita monthly household income less than 1000 yuan had lower score of overall cognitive function and its sub-domains(P<0. 05), and also had lower prevalence of MCI and its subtypes. The OR of MCI, naMCI-SD, aMCI-MD and naMCI-MD was 2. 38(95% CI 1. 98-2. 86), 1. 54(95% CI 1. 01-2. 34), 2. 30(95% CI 1. 65-3. 20) and 3. 11(95% CI 2. 07-4. 69) respectively in subjects aged ≥75 years versus those aged 55-64 years, and of MCI, naMCI-SD and aMCI-MD was 3. 02(95%CI 2. 48-3. 66), 4. 30(95%CI 2. 69-6. 88) and 2. 62(95%CI 1. 81-3. 79) respectively in those living in rural areas versus those living in city areas. Subjects with higher per capita monthly household income had lower ORs of MCI and its subtypes. CONCLUSION: The prevalence rate of MCI among people aged 55 and above in four provinces in China is at a relatively high level. In the studied 4 provinces of China, about 35% of Chinese middle-aged and elderly populations are affected by MCI. The status of overall cognitive function and its sub-domains of subjects aged 75 years and above, living rural areas and with lower per capita monthly household income are poor, and they may have a higher risk of MCI and its subtypes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".