A systematic review of short cognitive assessments for diagnosing dementia and MCI in clinical settings in Chinese‐speaking countries
Bibliographic record
Abstract
Abstract Background Mandarin Chinese is one of the most widely spoken languages in the world, and is used as a primary or secondary language in China, Hong Kong, Singapore and Taiwan. These countries differ in historical, economic and societal contexts so that cognitive assessments validated in one country may not be valid in another. The aims of this study are to compare the short diagnostic assessments (<15 minutes) used to assist in diagnosing dementia and Mild cognitive impairment (MCI) in Chinese‐speaking countries through rating the translation and cultural adaptation and reporting the sensitivity and specificity of assessments. Method We searched for terms relating to "assessments" and "dementia" or "MCI" and "Chinese‐speaking populations” in electronic bibliographic databases, including Embase, Ovid MEDLINE(R), PsycINFO, PsycTESTS, Web of Science, The Cochrane Library from inception to 3th July 2019. Two raters independently screened titles and abstracts, rated the translation and cultural adaption according to published guidelines by judging whether the details described are sufficient for replication and evaluated the quality of studies with a diagnostics critical appraisal sheet developed by the Centre for Evidence‐Based Medicine (CEBM). Result The searches retrieved 11763 articles, of which 74 articles were included after full‐text screening: 29 articles from China, 18 articles from Hong Kong, 12 articles from Singapore and 15 articles from Taiwan. We found 41 different short cognitive assessments used, but only two assessments ‐ the Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) have been validated in the four countries. The NUCOG cognitive screening tool in China specifically was shown to have the highest validity in screening 44 dementia diagnosed with DSM‐IV from 260 normal controls with a sensitivity of 100% and specificity of 98.5%. Reporting of cultural adaptation was achieved in approximately two third of the studies analysed. Conclusion There are wide range of tools available to assess cognitive impairment in the Chinese‐speaking populations most of them are not validated in all Chinese speaking populations. Their psychometric properties vary and translation and cultural adaptation procedures are poorly reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".