Montreal Cognitive Assessment Performance in Patients with Parkinson's Disease with “Normal” Global Cognition According to Mini‐Mental State Examination Score
Bibliographic record
Abstract
OBJECTIVES: To examine Montreal Cognitive Assessment (MoCA) performance in patients with Parkinson's disease (PD) with "normal" global cognition according to Mini-Mental State Examination (MMSE) score. DESIGN: A cross-sectional comparison of the MoCA and the MMSE. SETTING: Two movement disorders centers at the University of Pennsylvania and the Philadelphia Veterans Affairs Medical Center. PARTICIPANTS: A convenience sample of 131 patients with idiopathic PD who were screened for cognitive and psychiatric complications. MEASUREMENTS: Subjects were administered the MoCA and MMSE, and only subjects defined as having a normal age- and education-adjusted MMSE score were included in the analyses (N=100). As previously recommended in patients without PD, a MoCA score less than 26 was used to indicate the presence of at least mild cognitive impairment (MCI). RESULTS: Mean MMSE and MoCA scores+/-standard deviation were 28.8+/-1.1 and 24.9+/-3.1, respectively. More than half (52.0%) of subjects with normal MMSE scores had cognitive impairment according to their MoCA score. Impairments were seen in numerous cognitive domains, including memory, visuospatial and executive abilities, attention, and language. Predictors of cognitive impairment on the MoCA using univariate analyses were male sex, older age, lower educational level, and greater disease severity; older age was the only predictor in a multivariate model. CONCLUSION: Approximately half of patients with PD with a normal MMSE score have cognitive impairment based on the recommended MoCA cutoff score. These results suggest that MCI is common in PD and that the MoCA is a more sensitive instrument than the MMSE for its detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".