The Prevalence of Mild Cognitive Impairment among Chinese People: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) induced the majority number of dementia patients. The prevalence of MCI in China varied across studies with different screening tools and diagnostic criteria. OBJECTIVE: A systematic review and meta-analysis was conducted to estimate the pooled MCI prevalence among the population aged 55 years and older in China. METHODS: PubMed, EMBASE, CNKI, Wanfang, CQVIP, and CBMdisc were searched for studies on prevalence of MCI among Chinese elderly between January 1, 1980, and February 10, 2020. The quality assessment was conducted via external validity, internal validity, and informativity, the pooled prevalence was calculated through the random-effect model, and the homogeneity was evaluated by Cochran's Q test and I2. RESULTS: Fifty-three studies with 123,766 subjects were included. The pooled prevalence of MCI among Chinese elderly was 15.4% (95% CI: 13.5-17.4%). Subgroup analyses indicated that the prevalence calculated with different screening tools was 20.2% (95% CI: 15.1-25.9%) for Montreal Cognitive Assessment (MoCA) and 13.0% (95% CI: 10.7-15.5%) for Mini-Mental State Examination (MMSE). According to different diagnostic criteria, the prevalence was 14.8% (95% CI: 12.2-17.6%) for Petersen criteria, 15.0% (95% CI: 12.7-17.5%) for DSM-IV, and 21.2% (95% CI: 17.5-25.2%) for Chinese Expert Consensus on Cognitive Impairment (CECCI). Besides, women, older adults, illiterate people, rural residents, and those who lived with unhealthy lifestyles and morbidity showed higher prevalence. CONCLUSIONS: The prevalence of MCI in China was 15.4%, which varied by demographics, lifestyles, morbidity, screening tools, and diagnostic criteria. In further studies, screening tools and diagnosis criteria should be considered when estimating MCI prevalence.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".