Visual Cognitive Assessment Test (VCAT): A language‐neutral test to detect mild cognitive impairment and dementia in multinational cohorts
Bibliographic record
Abstract
BACKGROUND: Cognitive assessments are essential for the diagnosis of mild cognitive impairment (MCI) and dementia. However, existing tests are mostly developed in English-speaking cohorts. Hence, their application in multilingual populations will need translation which may affect their test psychometrics. VCAT is a language-neutral visual-based assessment that is developed to address this issue. While VCAT was validated in Southeast Asian countries, its performance in diverse language cohorts remains unclear. Here, we aim to compare the utility of VCAT with established screening tests, Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE), in distinguishing MCI and dementia from cognitively normal (CN) individuals in a multinational study. METHODS: This study supported by the Alzheimer's Association has recruited 670 participants (294 CN, 244 MCI, 132 Dementia) from Brazil, Canada, China, India, Korea and Singapore and recruitment is ongoing. We standardized the administration of MMSE, MoCA and VCAT across all study sites. Participants answered a questionnaire on their demographics and underwent cognitive assessments (MMSE, MoCA and VCAT) on the same day. The performance of VCAT in distinguishing MCI and dementia from CN were assessed within each sites using the area under the curve (AUC) analysis. RESULTS: The demographics, diagnosis and cognitive scores of the participants from each site were summarized in Table 1. The AUCs of VCAT in detecting MCI+Dementia vs CN were 0.979 for Brazil, 0.708 for Canada, 0.929 for China, 0.956 for Korea, 0.808 for India and 0.731 for Singapore. In comparison, the AUCs of MoCA in detecting MCI+Dementia vs CN were 0.771 for Brazil, 0.751 for Canada, 0.899 for China, 0.964 for Korea, 0.806 for India and 0.682 for Singapore, while the AUCs for MMSE in detecting MCI+Dementia vs CN were 0.896 for Brazil, 0.721 for Canada, 0.891 for China, 0.895 for Korea, 0.712 for India and 0.640 for Singapore. CONCLUSION: VCAT showed satisfactory discriminative validity in differentiating MCI+Dementia from CN participants within multinational, multilingual cohorts. VCAT was also comparable to the MoCA and MMSE. Further analysis in a larger cohort within our study will be performed to validate the utility of VCAT globally.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".