Cut-off points of the Portuguese version of the Montreal Cognitive Assessment for cognitive evaluation in Parkinson’s disease
Bibliographic record
Abstract
ABSTRACT. The Movement Disorder Society has published some recommendations for dementia diagnosis in Parkinson disease (PD), proposing the Montreal Cognitive Assessment (MOCA) as a cognitive screening tool in these patients. However, few studies have been conducted assessing the Portuguese version of this test in Brazil (MOCA-BR). Objective: the aim of the present study was to define the cut-off points of the MOCA-BR scale for diagnosing Mild Cognitive Impairment (PD-MCI) and Dementia (PD-D) in patients with PD. Methods: this was a cross-sectional, analytic field study based on a quantitative approach. Patients were selected after a consecutive assessment by a neurologist, after an extensive cognitive evaluation, and were classified as having normal cognition (PD-N), PD-MCI or PD-D. The MOCA-BR was then applied and 89 patients selected. Results: on the cognitive assessment, 30.3% were PD-N, 41.6% PD-MCI and 28.1% PD-D. The cut-off score on the MOCA-Br to distinguish PD-N from PD-D was 22.50 (95% CI 0.748-0.943) for sensitivity of 85.5% and specificity of 71.1%. The cut-off for distinguishing PD-D from MCI was 17.50 (95% CI 0.758-0.951) for sensitivity of 81.6% and specificity of 76%.
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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 | 0.003 | 0.009 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".