[A prevalence study on mild cognitive impairment among elderly populations in Zhejiang province].
Bibliographic record
Abstract
OBJECTIVE: To understand the prevalence of older people with mild cognitive impairment (MCI) in Zhejiang province and to provide the basis for elderly early detection and diagnosis of Alzheimer's disease (AD). METHODS: 1211 more than 60-year-old elderly populations were selected in Zhejiang province, and were given screening questionnaire by general information, the montreal cognitive assessment (MoCA) and mini-mental state examination (MMSE). RESULTS: MCI prevalence of elderly populations in Zhejiang was 20.7% and the AD prevalence was 4.5%. The patient's gender, age, education level, nature of work, sleep status, marital status, whether or not participating in physical exercise, having smoking and drinking habits, whether combined with hypertension and diabetes for MCI prevalence of the elderly were statistically significant (P < 0.05). CONCLUSION: We should pay attention to take appropriate measures in preventing the cognitive decline for populations as elderly, especially for women, senior, no spouse, engaged in manual labor, low education level, poor quality of sleep and no physical exercise, with smoking and drinking, combined with hypertension and diabetes.
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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".