Short Trends and Associated Factors of Cognitive Impairment in a Population at High Cardiovascular Risk 
Bibliographic record
Abstract
Abstract Background: It has been proposed that some risk factors of cardiovascular disease (CVD) are associated with and contribute to the development of all cause of dementia. Our aim was to examine the short trends and associated factors of cognitive impairment among a eastern Chinese population at high risk of CVD. Methods: We used a convenience sampling strategy to select 7 subdistricts in Zhejiang province, which was a part of China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE) project. Participants in 2018 (n=3089) and in 2020 (n=3082) at high risk of CVD in Zhejiang province with registry for the PEACE Project. Participants completed 3+ neuropsychological evaluations including of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Hospital Anxiety and Depression Scale (HAD) tests. Age-, gender- standardized prevalence rates were calculated based on the sixth population census for population of China. Cox proportional hazards regression models to estimate hazard ratios (HR) for the associations between the risk for all-cause dementia. Trial registration number was NCT02536456.Results: There was an increase prevalence of dementia and MCI among the individuals at high risk of CVD between 2018 and 2020. It showed that older age, lower education levels, having a medical history of hypertension, stroke and diabetes, having the family history of hypertension, heavy drinking, depression, and obesity were associated with dementia, while female might be a protective factor for dementia.Conclusion: More population-based strategies should be focusing on modifiable risk factors of dementia, such as CVD risk factors.
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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 | 0.001 | 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".