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; however, its precision in measurement in heterogeneous populations—especially among patients with Parkinson’s disease (PD)—remains underexplored. Methods: In this multicenter cross-sectional study, we examined the psychometric properties of the Brazilian Portuguese MoCA in 484 PD patients (age range, 26–90 years; mean ± SD, 59.9 ± 11.1 years; disease duration range, 1–35 years; mean ± SD, 8.7 ± 5.4 years) using Item Response Theory (IRT). The Graded Response Model (GRM) was employed to estimate item difficulty and discrimination parameters, and differential item functioning (DIF) concerning age and education was investigated via a Multiple Indicators Multiple Causes (MIMIC) model. Results: The MoCA demonstrated essential unidimensionality and robust model fit. GRM analyses revealed that items within the Attention and Naming domains had high discrimination, indicating sensitivity to subtle cognitive deficits, while Memory items exhibited lower discrimination. Orientation items showed low difficulty thresholds, suggesting a propensity for ceiling effects. The MIMIC model further indicated that age and education significantly influenced overall scores: increasing age was associated with lower performance, whereas higher educational attainment correlated with better outcomes, particularly in Memory Recall and Executive/Visuospatial domains, even after accounting for their modest inverse relationship. Conclusions: Our findings support the validity of the Brazilian Portuguese MoCA for cognitive screening in PD while highlighting item-level biases linked to age and education. These results advocate for using education-adjusted norms and computerized scoring algorithms that incorporate item parameters, ultimately enhancing the reliability and fairness of cognitive assessments in diverse clinical populations.
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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".