A Validation Study of the Remotely Administered Montreal Cognitive Assessment Tool in the Elderly Japanese Population
Bibliographic record
Abstract
Background: In an aging society, neuropsychological testing using video teleconferencing (VTC) is increasingly important. Despite the potential benefit of a VTC-administered Montreal Cognitive Assessment Tool (MoCA) to detect cognitive decline, only a limited number of studies have investigated this tool's reliability. Therefore, we aimed to evaluate the reliability of VTC-administered MoCA compared with face-to-face (FTF)-administered MoCA among elderly Japanese participants. Moreover, we examined participants' satisfaction with VTC-administered MoCA. Methods: Participants ≥60 years of age with and without cognitive impairment (i.e., those with mild cognitive impairment [MCI], those with dementia, and healthy controls [HC]) were assessed with VTC- and FTF-administered MoCA at an interval of >2 weeks and <3 months. The order effect (VTC first vs. FTF first) and time effect (first vs. second testing session), as well as several covariates such as age and years of education were controlled. Intraclass correlation coefficients (ICCs) were calculated using a mixed-effects model to assess the agreement between the two (VTC- vs. FTF-administered) groups. Participants' satisfaction with VTC-administered MoCA was examined using a Likert scale asking seven questions. Results: We included 73 participants in the study (36 men; age, 76.3 ± 7.5 years). The ICC for the MoCA total score was high in the entire sample (0.85), whereas ICCs were moderate to high for the subgroups (MCI: 0.82, dementia: 0.82, and HC: 0.53). Furthermore, we found good overall participant satisfaction with VTC-administered MoCA. Discussion: VTC-administered MoCA appears viable as an alternative to FTF-administered MoCA, although further replication studies with larger sample sizes are needed.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".