Large-scale evidence for the validity of remote MoCA administration among people with cerebellar ataxia
Bibliographic record
Abstract
Objective: For over half a century, studies of rare diseases using in-person cognitive tools have faced challenges, such as long study periods and small sample sizes (e.g. n = 10). The Montreal Cognitive Assessment (MoCA) was widely employed to assess mild cognitive impairment (MCI). We aimed to validate a modified online version of the MoCA in a large sample of a rare disease (population prevalence < .01%). Method: First, we analyzed 20 previous findings (n = 1,377), comparing the MoCA scores between large groups of neurotypically healthy (NH; n = 837) and cerebellar ataxia (CA; n = 540), where studies were conducted in-person. Second, we administered the MoCA in-person to a group of NH (n = 41) and a large group of CA (n = 103). Third, we administered a video conferencing version of the MoCA to NH (n = 38) and a large group of CA (n = 83). Results: We observed no performance differences between online and in-person MoCA administration in the NH and CA groups (p > .05, η2 = 0.001), supporting reliability. Additionally, our online CA group had lower MoCA scores than the NH group (p < .001, Hedges’ g = 0.68). This result is consistent with previous studies, as demonstrated by our forest plot across 20 previous in-person findings, supporting construct validity. Conclusion: The results indicate that an online screening tool is valid in a large sample of individuals with CA. Online testing is not only time and cost-effective, but facilitates disease management and monitoring, ultimately enabling early detection of MCI.
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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 | 0.001 |
| 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 | 0.001 |
| 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".