Some aspects of the validity of the Montreal Cognitive Assessment (MoCA)for evaluating cognitive impairment in Brazilian patients with Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is a short global cognitive scale, and some studies suggest it is useful for evaluating cognition in patients with Parkinson's disease (PD). However, its accuracy has been questioned in studies involving patients with low education. OBJECTIVE: We sought to assess whether some of the MoCA subtests contribute to the low accuracy of the test. METHODS: We performed a cross-sectional retrospective analysis of clinical data in a cohort of 71 patients with PD, most with less than 8 years of education. Patients were examined using the MDS-UPDRS, Hoehn and Yahr and the MoCA. The data were analyzed using mainly descriptive statistics. RESULTS: We analyzed the data of 66 patients that were not demented according to the clinical evaluation and classified them using the proposed cut-off MoCA scores for diagnosis of MCI and dementia. Thirteen patients (19.7%) were classified as having normal cognition, 24 (36.3%) MCI and 29 (43.9%) dementia. Patients with dementia had longer disease duration (p=0.016) and lower education (p=0.0001). Total MoCA scores had a an almost normal distribution with a wide range of scores and only one maximum score. Performance on the MoCA was highly correlated with education (correlation coefficient=0.66, p=0.0001). At least five of the 10 MoCA subtests showed significant floor effects. CONCLUSION: We believe that some of the MoCA subtests may be too difficult to be completed by PD patients with low educational level, thus contributing to the test's poor diagnostic accuracy.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".