Validation of a Cognitive Self-Assessment Tool Simulating Japan's Official Digital Test for Older Drivers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».