Validity of the MoCA and MMSE in the detection of MCI and dementia in Parkinson disease
Bibliographic record
Abstract
BACKGROUND: Due to the high prevalence of mild cognitive impairment (MCI) and dementia in Parkinson disease (PD), routine cognitive screening is important for the optimal management of patients with PD. The Montreal Cognitive Assessment (MoCA) is more sensitive than the commonly used Mini-Mental State Examination (MMSE) in detecting MCI and dementia in patients without PD, but its validity in PD has not been established. METHODS: A representative sample of 132 patients with PD at 2 movement disorders centers was administered the MoCA, MMSE, and a neuropsychological battery with operationalized criteria for deficits. MCI and PD dementia (PDD) criteria were applied by an investigator blinded to the MoCA and MMSE results. The discriminant validity of the MoCA and MMSE as screening and diagnostic instruments was ascertained. RESULTS: Approximately one third of the sample met diagnostic criteria for a cognitive disorder (12.9% PDD and 17.4% MCI). Mean (SD) MoCA and MMSE scores were 25.0 (3.8) and 28.1 (2.0). The overall discriminant validity for detection of any cognitive disorder was similar for the MoCA and the MMSE (receiver operating characteristic area under the curve [95% confidence interval]): MoCA (0.79 [0.72, 0.87]) and MMSE (0.76 [0.67, 0.85]), but as a screening instrument the MoCA (optimal cutoff point = 26/27, 64% correctly diagnosed, lack of ceiling effect) was superior to the MMSE (optimal cutoff point = 29/30, 54% correctly diagnosed, presence of ceiling effect). CONCLUSIONS: The Montreal Cognitive Assessment, but not the Mini-Mental State Examination, has adequate psychometric properties as a screening instrument for the detection of mild cognitive impairment or dementia in Parkinson disease. However, a positive screen using either instrument requires additional assessment due to suboptimal specificity at the recommended screening cutoff point.
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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 | 0.017 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".