Mild Cognitive Impairment: Vascular Risk Factors in Community Elderly in Four Cities of Hebei Province, China
Bibliographic record
Abstract
BACKGROUND: Evidence has demonstrated that vascular risk factors (VRFs) contribute to mild cognitive impairment (MCI) in the elderly population. Because of the race and different diagnosis standard, there is still no definitive conclusions. OBJECTIVE: To estimate the VRFs and potential protective factors for MCI in elderly population living in the community in North China. METHODS: A total of 3136 participants entered the study. They were screened for hypertension, coronary heart disease (CHD), and cerebrovascular disease (CVD). Cognitive function was assessed with Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The diagnosis of MCI was made according to Petersen's criteria. We investigated the relationship between vascular risk factors, potential protective factors and MCI. RESULTS: A total of 2511 (80%) participant belonged to normal group and 625 (20%) participants showed MCI. Multiple logistic regression analysis demonstrated that stroke and diabetes, but not hypertension or CHD was associated with MCI. Besides, exercise habit could lower the risk of MCI. CONCLUSIONS: Vascular Risk Factors, including stroke and diabetes, rather than hypertension and CHD are independent risk factors of MCI. Involvement in physical activities seems to reduce the risk of MCI.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".