Validation of a Cognitive Self-Assessment Tool Simulating Japan's Official Digital Test for Older Drivers
Bibliographic record
Abstract
Background A mandatory tablet-based cognitive function test for older drivers in Japan is employed for formal assessment only, terminating once a passing score is achieved and precluding a complete assessment. To bridge this gap between formal assessment and the need for self-preparation among older drivers, Nippontect Systems Co., Ltd., Japan, developed “MOGI, ” a tablet-based application that allows users to experience the entire official test for self-assessment purposes. The objective of this study was to validate “MOGI” by examining its correlation with the Mini-Mental State Examination-Japanese version (MMSE-J). Methods We conducted a cross-sectional study at the Minato City Silver Human Resources Center in Tokyo and among outpatients at the Oyama Orthopedics and Internal Medicine Clinic in Tochigi Prefecture. The required sample size was calculated by assuming a specific correlation coefficient, significance level, and power. Community-dwelling volunteers and individuals clinically diagnosed with mild cognitive impairment (MCI) or mild-to-moderate dementia participated from February 3 to 17, 2025. All diagnoses were made by a neurologist based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. Participants completed both the “MOGI” application, assessing memory and judgment via cued recall and time orientation tasks, and the MMSE-J. The agreement between automated scoring by “MOGI” and manual scoring by qualified staff was evaluated using the intraclass correlation coefficient (ICC). Spearman’s rank correlation was used to examine the relationship between “MOGI” and MMSE-J scores, and differences in “MOGI” scores among MMSE-J-based groups (≥28, 24-27, and ≤23) were evaluated. Results The required sample size was 37, assuming a 0.5 correlation coefficient, 5% significance level, and 90% power. A total of 42 participants, including 17 male and 25 female participants, were included in the final analysis; their mean age was 76.4±8.2 years. Excellent agreement was observed between the automated and manual scoring systems (ICC = 0.97, 95% CI: 0.94-0.98). A significant, strong positive correlation was observed between the “MOGI” total score and the MMSE-J score (ρ = 0.64, p<0.001). “MOGI” also demonstrated excellent discriminative ability, with significant differences in scores among the three MMSE-J-based groups (p<0.001 among the three groups; p<0.05 for ≥28 vs. 24-27; p<0.001 for ≥28 vs. ≤23; p<0.01 for 24-27 vs. ≤23). Conclusion “MOGI” exhibits robust validity as a cognitive assessment tool, supported by a reliable automated scoring system. By providing a comprehensive assessment experience unavailable in the official test, “MOGI” serves as a valuable complementary tool for practice, self-monitoring, and a more nuanced understanding of one's cognitive function, potentially contributing to the early detection of cognitive decline.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".