CONVERTING MOCA-J TO MMSE-J IN COMMUNITY-DWELLING JAPANESE OLDER ADULTS: A PILOT STUDY
Bibliographic record
Abstract
This study aimed to develop a conversion table for the Japanese version of the Montreal Cognitive Assessment (MoCA-J) to the Japanese version of the Mini-Mental State Examination (MMSE-J) to facilitate prediction of the MMSE-J score from the MoCA-J score. Participants (N = 121) were community-dwelling older adults (M = 74.12, SD = 4.73, age 61–84) who were able to visit the university laboratory alone, having no diagnosis of dementia. Their cognitive performance was assessed by MoCA-J and MMSE-J. We developed the MMSE-J conversion table from the row MoCA-J scores using the equipercentile equating with log-linear smoothing. A Bland-Altman plot displayed a nonsignificant systematic bias between the raw and converted MMSE scores. The conversion table presented high accuracy, with 85.8% of converted MMSE-J scores falling within two points of raw scores. In addition, our conversion table demonstrated the advantage of using the MoCA-J for early detection of cognitive decline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".