A comparison of the mini mental state exam to the montreal cognitive assessment in identifying cognitive deficits in Parkinson's disease
Bibliographic record
Abstract
Dementia is an important and increasingly recognized problem in Parkinson's disease (PD). The mini-mental state examination (MMSE) often fails to detect early cognitive decline. The Montreal cognitive assessment (MoCA) is a brief tool developed to detect mild cognitive impairment that assesses a broader range of domains frequently affected in PD. The scores on the MMSE and the MoCA were compared in 88 patients with PD. A pronounced ceiling effect was observed with the MMSE but not with the MoCA. The range and standard deviation of scores was larger with the MoCA(7-30, 4.26) than with the MMSE(16-30, 2.55). The percentage of subjects scoring below a cutoff of 26/30 (used by others to detect mild cognitive impairment) was higher on the MoCA (32%) than on the MMSE (11%) (P < 0.000002). Compared to the MMSE, the MoCA may be a more sensitive tool to identify early cognitive impairment 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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".