Visual Cognitive Assessment Test (VCAT): Utility in detecting early dementia in diverse language cohorts
Bibliographic record
Abstract
Abstract Background Cognitive tests play a crucial role in evaluating mild cognitive impairment (MCI) and dementia. As most tests are originally developed in English‐speaking cohorts, their application in multilingual populations will need to be translated and may affect the test psychometrics. Therefore, the VCAT was developed as a language‐neutral visual‐based assessment to mitigate this issue. Although the VCAT has been validated in Southeast Asian countries, its performance in wider 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. Method This collaboration between Canada, China, India, Korea and Singapore recruited 614 participants (306 CN, 212 MCI, 94 Dementia). The administration of the cognitive tests were standardized across all study sites. Participants underwent the MMSE, MoCA, and VCAT on the same day, with their scores and demographic data (age, gender, years of education, ethnicity, primary written and spoken language) collected in the centers respectively. The performance of VCAT in distinguishing MCI and dementia from CN were assessed within each sites using the area under the curve (AUC). Result Baseline demographics of the participants in each site were summarized in Table 1. The mean (SD) MMSE, MoCA and VCAT scores in each diagnostic group across all countries were summarized in Table 2. The mean (SD) VCAT scores across all countries for CN, MCI and dementia were 25.91 (3.14); 21.00 (5.36) and 11.91 (4.79) respectively. Corresponding scores for MoCA were 26.09 (2.76); 22.62 (4.43); 14.02 (5.17) and corresponding scores for MMSE were 28.34 (1.79); 26.55 (2.90); 18.91 (4.81). The AUC of VCAT in detecting cognitive impairment (MCI+Dementia) was found to be 0.83 (95%CI 0.80 to 0.86), which is comparable to the MoCA 0.82 (95%CI 0.78 to 0.85) and the MMSE 0.78 (95%CI 0.74 to 0.81). Conclusion VCAT was comparable to the MoCA and MMSE in distinguishing cognitive impairment from CN in a multinational, multilingual population. Further studies with wider language populations are needed to further validate the utility of VCAT globally.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".