The Montreal Cognitive Assessment and Neurobehavioral Cognitive Status Examination are useful for screening mild cognitive impairment in Japanese patients with Parkinson's disease
Bibliographic record
Abstract
Abstract Aim Diagnosis of mild cognitive impairment (MCI) in Parkinson's disease (PD; PD‐MCI) can be difficult. We examined whether the Japanese version of the Montreal Cognitive Assessment (MoCA‐J) and the Neurobehavioral Cognitive Status Examination (COGNISTAT‐J) were suitable to screen PD‐MCI. Methods A total of 50 patients with PD and PD with dementia, took the Mini‐Mental State Examination (MMSE), MoCA‐J and COGNISTAT‐J (except 3 patients) tests. Impairment in each cognitive domain on the tests was then compared between groups. Results Of 25 patients with a high MMSE score of 27 or above, 13 had a MoCA‐J score of below 25, and showed significantly lower scores than 12 patients with MoCA‐J scores of 25 or above in the visuospatial, executive and memory domains of MoCA. Of the 25 patients with a high MMSE score, seven had four or more impaired subtests of the 10 subtests of COGNISTAT‐J. The seven patients showed significantly lower scores in the subtests of construction, calculation and similarity compared with the 18 patients with less than four impaired subtests. In patients with a high MMSE score and less than four impaired subtests on the COGNISTAT‐J, construction and memory were more impaired. Executive, visuospatial and memory abilities are characteristically impaired in PD‐MCI, therefore both MoCA‐J and COGNISTAT‐J detected characteristics of PD‐MCI in patients with a high MMSE score. Conclusion Both MoCA and COGNISTAT are sensitive tools for screening for PD‐MCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".