Correlation Between The Indonesian Versions of Montreal Cognitive Assessment (MoCA-INA) and Visual Cognitive Assessment Test (VCAT-INA) as Cognitive Screening Tools
Bibliographic record
Abstract
Background and purpose: Screening for cognitive impairment has become increasingly important as the population ages, especially using tools that is not mainly affected my translational process so it can be used in multilingual population. The aim of this study was to determine the correlation between the Indonesian version of Montreal Cognitive Assessment (MoCA-INA) and Visual Cognitive Assessment Test (VCAT-INA) as cognitive screening tools.Methods: This was a cross sectional study involving subjects recruited for cognitive screening in general population and memory clinic Adam Malik General Hospital Medan Indonesia between December 2019 and April 2020. All subjects underwent physical and neurologic examination and cognitive assessment including MoCA-INA and VCAT-INA, that was adapted from the original version.Results: A total of 104 subjects were studied, consisted of 41 (39.4%) males and 63 (60.4%) females. The mean age of subjects was 64.4±10.07 years and ranged from 41-82 years. Most of the subjects had 12 years of education (45 subjects; 43.3%). Most of the subjects had abnormal MoCA-INA and VCAT-INA scores. Both scores showed comparable result but VCATINA showed lower average and a broader range of scores. There was a strong positive significant correlation between the scores (r=0.815; p < 0.001).Conclusions: MoCAINA score is strongly correlated with VCAT-INA score. As visual-based test, VCAT-INA can be applied as a cognitive screening tool in daily clinical practice without significant language barrier.International Journal of Human and Health Sciences Vol. 06 No. 01 January’22 Page: 47-54
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".