Brief screening tool for mild cognitive impairment in older Japanese: Validation of the Japanese version of the Montreal Cognitive Assessment
Bibliographic record
Abstract
AIM: The Montreal Cognitive Assessment (MoCA), developed by Dr Nasreddine (Nasreddine et al. 2005), is a brief cognitive screening tool for detecting older people with mild cognitive impairment (MCI). We examined the reliability and validity of the Japanese version of the MoCA (MoCA-J) in older Japanese subjects. METHODS: Subjects were recruited from the outpatient memory clinic of Tokyo Metropolitan Geriatric Hospital or community-based medical health check-ups in 2008. The MoCA-J, the Mini-Mental State Examination (MMSE), the revised version of Hasegawa's Dementia Scale (HDS-R), Clinical Dementia Rating (CDR) scale, and routine neuropsychological batteries were conducted on 96 older subjects. Mild Alzheimer's disease (AD) was found in 30 subjects and MCI in 30, with 36 normal controls. RESULTS: The Cronbach's alpha of MoCA-J as an index of internal consistency was 0.74. The test-retest reliability of MoCA, using intraclass correlation coefficient between the scores at baseline survey and follow-up survey 8 weeks later was 0.88 (P < 0.001). MoCA-J score was highly correlated with MMSE (r = 0.83, P < 0.001), HDS-R (r = 0.79, P < 0.001) and CDR (r = -0.79, P < 0.001) scores. The areas under receiver-operator curves (AUC) for predicting MCI and AD groups by the MoCA-J were 0.95 (95% confidence interval [CI] = 0.90-1.00) and 0.99 (95% CI = 0.00-1.00), respectively. The corresponding values for MMSE and HDS-R were 0.85 (95% CI = 0.75-0.95) and 0.97 (95% CI = 0.00-1.00), and 0.86 (95% CI = 0.76-0.95) and 0.97 (95% CI = 0.00-1.00), respectively. Using a cut-off point of 25/26, the MoCA-J demonstrated a sensitivity of 93.0% and a specificity of 87.0% in screening MCI. CONCLUSION: The MoCA-J could be a useful cognitive test for screening MCI, and could be recommended in a primary clinical setting and for geriatric health screening in the community.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".