Comparing the Montreal Cognitive Assessment with Mini‐Mental State Examination in Japanese Parkinson's disease patients
Bibliographic record
Abstract
Abstract Background and Aim The Montreal Cognitive Assessment (Mo CA ) is the most suitable measure for screening cognitive impairment in Parkinson's disease ( PD ). However, the utility of the Mo CA has not been documented sufficiently, especially in Asian populations. The present multicenter study included a large number of Japanese patients, and compared Mini‐Mental State Examination ( MMSE ) and Mo CA scores in PD patients. Methods We carried out a cross‐sectional study. Idiopathic PD patients ( n = 304; age 70.6 ± 8.3 years (mean ± SD); disease duration 6.6 ± 5.1 years; Hoehn and Yahr stage 2.7 ± 0.7) were registered at 13 participating hospitals, and their clinical/neurological/cognitive features were examined using Japanese versions of the MMSE and Mo CA . Results The MMSE and Mo CA scores were 26.3 ± 3.6 (range 12–30) and 20.9 ± 5.0 (range 5–30), respectively, and showed a strong correlation ( R 2 = 0.74, P < 0.001) with each other. A MMSE score of <26 was observed in 35% of the participants. A Mo CA score of <21 had 89% sensitivity and 83% specificity, comparable with a MMSE score of <26. The two scores were correlated with age ( R 2 = 0.12 and 0.20, respectively; P < 0.0001), but not with Hoehn and Yahr stage or disease duration. Conclusions One‐third of the patients had a MMSE score of <26, a diagnostic criterion of PD with dementia. A Mo CA score of <21 seemed comparable with a MMSE score of <26. The two scores were correlated with age, rather than severity of motor symptoms, suggesting that cognitive decline might be independent of motor decline in PD .
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".