Diagnostic accuracy of dementia screening tools in the Chinese population: a systematic review and meta-analysis of 167 diagnostic studies
Bibliographic record
Abstract
BACKGROUND: The rate of undetected dementia is high in China. However, the performance of dementia screening tools may differ in the Chinese population due to the lower education level and cultural diversity. This study aimed to evaluate the diagnostic accuracy of dementia screening tools in the Chinese population. METHODS: Eleven electronic databases were searched for studies evaluating the diagnostic accuracy of dementia screening tools in older Chinese adults. The overall diagnostic accuracy was estimated using bivariate random-effects models, and the area under the summary receiver operating characteristic curve was presented. RESULTS: One hundred sixty-seven studies including 81 screening tools were identified. Only 134 studies qualified for the meta-analysis. The Mini-Mental State Examination (MMSE) was the most commonly studied tool, with a combined sensitivity (SENS) and specificity (SPEC) of 0.87 (95%CI 0.85-0.90) and 0.89 (95%CI 0.86-0.91), respectively. The Addenbrooke's Cognitive Examination-Revised (ACE-R) (SENS: 0.96, 95%CI 0.89-0.99; SPEC: 0.96, 95%CI 0.89-0.98) and Montreal Cognitive Assessment (MoCA) (SENS: 0.93, 95%CI 0.88-0.96; SPEC: 0.90, 95%CI 0.86-0.93) showed the highest performance. The General Practitioner Assessment of Cognition (GPCOG), Hasegawa's Dementia Scale and Cognitive Abilities Screening Instrument had performances comparable to that of the MMSE. The cut-off scores ranged widely across studies, especially for the MMSE (range: 15-27) and MoCA (range: 14-26). CONCLUSIONS: A number of dementia screening tools were validated in the Chinese population after cultural and linguistical adaptations. The ACE-R and MoCA had the best diagnostic accuracy, whereas the GPCOG, with an administration time < 5 minutes, could be considered as a rapid screening tool.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".