Floor and ceiling effects on the Montreal Cognitive Assessment in patients with Parkinson’s disease in Brazil
Bibliographic record
Abstract
Parkinson's disease (PD) is a common neurodegenerative disease associated with cognitive impairment. The Montreal Cognitive Assessment (MoCA) has been used as a recommended global cognition scale for patients with PD, but there are some concerns about its application, partially due to the floor and ceiling effects. Objective: To explore the floor and ceiling effects on the MoCA in patients with PD in Brazil. Methods: Cross-sectional study with data from patients with PD from five Brazilian Movement Disorders Clinics, excluding individuals with a possible diagnosis of dementia. We analyzed the total score of the MoCA, as well as its seven cognitive domains. The floor and ceiling effects were evaluated for the total MoCA score and domains. Multivariate analyses were performed to detect factors associated with floor and ceiling effects. Results: We evaluated data from 366 patients with PD and approximately 19% of individuals had less than five years of education. For the total MoCA score, there was no floor or ceiling effect. There was a floor effect in the abstraction and delayed memory recall domains in 20% of our sample. The ceiling effect was demonstrated in all domains (80.8% more common in naming and 89% orientation), except delayed recall. Education was the main factor associated with the floor and ceiling effects, independent of region, sex, age at evaluation, and disease duration. Conclusion: The floor and ceiling effects are present in specific domains of the MoCA in Brazil, with a strong impact on education. Further adaptations of the MoCA structure for underrepresented populations may reduce these negative effects.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".