Cognitive assessment tools in Asia: a systematic review
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Bibliographic record
Abstract
BACKGROUND: The prevalence of dementia is increasing in Asia than in any other continent. However, the applicability of the existing cognitive assessment tools is limited by differences in educational and cultural factors in this setting. We conducted a systematic review of published studies on cognitive assessments tools in Asia. We aimed to rationalize the results of available studies which evaluated the validity of cognitive tools for the detection of cognitive impairment and to identify the issues surrounding the available cognitive impairment screening tools in Asia. METHODS: Five electronic databases (CINAHL, MEDLINE, Embase, Cochrane Library, and Science Direct) were searched using the keywords dementia Or Alzheimer Or cognitive impairment And screen Or measure Or test Or tool Or instrument Or assessment, and 2,381 articles were obtained. RESULTS: Thirty-eight articles, evaluating 28 tools in seven Asian languages, were included. Twenty-nine (76%) of the studies had been conducted in East Asia with only four studies conducted in South Asia and no study from northern, western, or central Asia or Indochina. Local language translations of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were assessed in 15 and six studies respectively. Only three tools (the Korean Dementia Screening Questionnaire, the Picture-based Memory Intelligence Scale, and the revised Hasegawa Dementia Screen) were derived de novo from Asian populations. These tools were assessed in five studies. Highly variable cut-offs were reported for the MMSE (17-29/30) and MoCA (21-26/30), with 13/19 (68%) of studies reporting educational bias. CONCLUSIONS: Few cognitive assessment tools have been validated in Asia, with no published validation studies for many Asian nations and languages. In addition, many available tools display educational bias. Future research should include concerted efforts to develop culturally appropriate tools with minimal educational bias.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it