Remote assessment of cognition in Parkinson’s disease and Cerebellar Ataxia: the MoCA test in English and Hebrew
Bibliographic record
Abstract
There is a critical need for accessible neuropsychological testing for basic research and translational studies worldwide. Traditional in-person neuropsychological studies are inherently difficult to conduct because testing requires the recruitment and participation of individuals with neurological conditions. Consequently, studies are often based on small sample sizes, are highly time-consuming, and lack diversity. To address these challenges, in the last decade, the utilization of remote testing platforms has demonstrated promising results regarding the feasibility and efficiency of collecting patient data online. Herein, we tested the validity and generalizability of remote administration of the Montreal Cognitive Assessment (MoCA) test. We administered the MoCA to English and Hebrew speakers from three different populations: Parkinson's disease, Cerebellar Ataxia, and healthy controls via video conferencing. First, we found that the online MoCA scores do not differ from traditional in-person studies, demonstrating convergent validity. Second, the MoCA scores of both our online patient groups were lower than controls, demonstrating construct validity. Third, we did not find differences between the two language versions of the remote MoCA, supporting its generalizability to different languages and the efficiency of collecting binational data (USA and Israel). Given these results, future studies can utilize the remote MoCA, and potentially other remote neuropsychological tests to collect data more efficiently across multiple different patient populations, language versions, and nations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".