Low Sensitivity of the Mini-Mental State Examination for Cognitive Assessment of Brazilian Patients With Parkinson Disease
Bibliographic record
Abstract
INTRODUCTION: Recent publications have highlighted the low sensitivity of the Mini-Mental State Examination (MMSE) for the cognitive assessment of patients with Parkinson disease (PD). The Montreal Cognitive Assessment (MoCA), otherwise, has shown greater sensitivity when compared to the MMSE. Based on this, we have searched for the cognitive impairment measurable by the MoCA and the functional performance on activities of daily living in a sample of Brazilian patients with PD and normal MMSE. We hypothesized that the low sensitivity of the MMSE, already shown by other authors, could be replicated in a low-income country. OBJECTIVE: To describe the performance on the MoCA and the dependence on third parties for activities of daily living in a sample of Brazilian patients with PD and normal MMSE. METHODS: We evaluated 43 volunteers with PD and normal MMSE considering the Brazilian cutoffs. Cognitive performance was assessed through the MoCA and functional performance through a modified version of the Disability Assessment for Dementia Scale. RESULTS: Despite normal score on the MMSE, considering the Brazilian cutoffs, 62.7% of the volunteers performed below the literature cutoff for the MoCA (26 points). Furthermore, 30.2% had dependence on third party for activities of daily living. By using a strict cutoff for the MMSE (26 points), 56.7% performed below the MoCA cutoff and 24.3% had dependence for activities of daily living. CONCLUSIONS: Our findings confirm the limitations of the MMSE for the cognitive screening of patients with PD in a low-income country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".