Investigating the measurement precision of the montreal cognitive assessment (MoCA) for cognitive screening in parkinson’s disease through item response theory
Bibliographic record
Abstract
Background: The Montreal Cognitive Assessment (MoCA) is widely used to evaluate global cognitive function in older Brazilian adults. However, concerns persist regarding its applicability in non-homogeneous socio-demographic groups. This study scrutinizes the Brazilian version of the MoCA, focusing on its measurement properties in a diverse cohort of patients with Parkinson's disease (PD). Purpose: This study examined the psychometric properties of the Brazilian Portuguese MoCA in a heterogeneous sample of PD patients using item response theory (IRT) methods. Material and Methods: In a multicenter cross-sectional study, 484 PD patients aged 26-90 years (mean ± SD, 59.9 ± 11.1 years), with disease durations ranging from 1 to 35 years (mean ± SD, 8.7 ± 5.4 years), underwent MoCA testing. IRT analyses, including the Graded Response Model, evaluated item parameters, such as difficulty (location) and discrimination. Differential item functioning was analyzed vis-à-vis age and education using multiple indicators multiple causes (MIMIC) modeling. Results: The MoCA exhibited essential unidimensionality and satisfactory model fit. Attention and naming demonstrated high discrimination. Orientation and naming items were less challenging. Multiple domains showed differential item functioning related to age and education, underscoring the necessity of considering background characteristics when interpreting total scores. Conclusion: This study enriches validity evidence for the MoCA in PD by providing a detailed analysis of its measurement properties and sources of score bias. Tailoring test content and norms based on education and establishing computerized scoring algorithms leveraging item parameters may optimize the tool’s reliability and fairness. Refinements to mitigate differential item functioning could enable precise cognitive screening across diverse socio-demographic backgrounds.
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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 | 0.048 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".