The Validity of the Thai Electronic Version of the Montreal Cognitive Assessment (MoCA)
Bibliographic record
Abstract
Abstract Electronic cognitive assessment tools are essential in screening and managing patients’ disease progression in clinical practices. Neurocognitive disorders, particularly mild cognitive impairment (MCI) and Alzheimer’s Dementia (AD), can be assessed using the Montreal Cognitive Assessment (MoCA) [1, 2]. A digital version of the MoCA could help reduce the burden on healthcare providers, increase accessibility, and maximize its utility, especially in countries that lack medical staff such as Thailand. This pilot study aimed to establish the concurrent validity between the electronic version of the MoCA‐Thai (eMoCA‐Thai), developed by the authors of this paper, to the original paper‐and‐pencil version (MoCA‐Thai) in adults who are cognitively normal (CN), have MCI, and have AD. 54 adults between the ages of 60 to 90 attending a dementia clinic were administered both the eMoCA‐Thai and the standard MoCA‐Thai one week apart. The primary outcome measures include the total scores and all subscale scores of the two test versions. Correlations and differences between scores were analyzed using concordance correlation coefficients (CCC) and various parametric t‐tests. Results show that there is high correlation for total MoCA scores with a CCC of 0.919 and a mean difference of ‐0.204 (‐6.311, 5.904). All the cognitive subdomain scores had moderate to high CCC of more than 0.4. The differences in average total score (22.81±7.49 for the MoCA‐Thai and 23.02±7.99 for the eMoCA‐Thai) were not statistically significant (p = 0.633), indicating no differences between the MoCA scores of both versions. When comparing total MoCA scores between participant groups, both CN and MCI had acceptable correlations (CCC >0.2) with a mean difference of 0.355 (‐4.720, 5.429) and ‐1.643 (9.001, 5.715), respectively, whilst AD had excellent correlation (CC >0.8) with a mean difference of 0.111 (‐6.496, 6.717). Subdomain scores between participant groups were not statistically significantly different except for Delayed Recall for all participants combined (p<0.05). In conclusion, there is adequate concurrent validity between the MoCA‐Thai and eMoCA‐Thai, making it a useful tool to help improve workflow and increase accessibility.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".