A novel language-neutral Visual Cognitive Assessment Test (VCAT): validation in four Southeast Asian countries
Bibliographic record
Abstract
Cognitive screeners are imperative for early diagnosis of dementia. The Visual Cognitive Assessment Test (VCAT) is a language-neutral, visual-based test which has proven useful for a multilingual population in a single-center study. However, its performance utility is unknown in a wider and more diverse Southeast Asian cohort. We recruited 164 healthy controls (HC) and 120 cognitively impaired (CI) subjects- 47 mild cognitive impairment (MCI) and 73 mild Alzheimer's disease (AD) dementia participants, from four countries between January 2015 and August 2016 to determine the usefulness of a single version of the VCAT, without translation or adaptation, in a multinational, multilingual population. The VCAT was administered along with established cognitive evaluation. The VCAT, without local translation or adaptation, was effective in discriminating between HC and CI subjects (MCI and mild AD dementia). Mean (SD) VCAT scores for HC and CI subjects were 22.48 (3.50) and 14.17 (5.05) respectively. Areas under the curve for Montreal Cognitive Assessment (0.916, 95% CI 0.884–0.948) and the VCAT (0.905, 95% CI 0.870–0.940) in discriminating between HCs and CIs were comparable. The multiple languages used to administer VCAT in four countries did not significantly influence test scores. The VCAT without the need for language translation or cultural adaptation showed satisfactory discriminative ability and was effective in a multinational, multilingual Southeast Asian population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".