Factor structure of the <scp>Montreal Cognitive Assessment</scp> in <scp>Parkinson</scp> disease
Bibliographic record
Abstract
OBJECTIVES: The Montreal Cognitive Assessment (MoCA) is a common tool for screening mild cognitive impairment (MCI) and dementia. Studies in multiple clinical groups provide evidence for various factor structures mapping to different cognitive domains. We tested the factor structure of the MoCA in a large cohort of early Parkinson disease (PD). MATERIALS AND METHODS: Complete MoCA data were available from an observational cohort study for 1738 patients with recent-onset PD (64.6% male, mean age 67.6, SD 9.2). Confirmatory factor analysis (CFA) was applied to test previously defined two-factor, six-factor, and three-factor models in the full sample and in a subgroup with possible cognitive impairment (MoCA < 26). Secondary analysis used exploratory factor analysis (EFA; principal factors with oblique rotation). RESULTS: /df 17.77, root mean square error of approximation [RMSEA] 0.10, comparative fit index [CFI] 0.74, Tucker-Lewis index [TLI] 0.69, standardised root mean square residual [SRMR] 0.07) indicated poorer fit than did previous studies. Findings were similar in the two-factor and three-factor models. EFA suggested an alternative six-factor solution (short-term recall, visuospatial-executive, attention/working memory, verbal-executive, orientation, and expressive language), although CFA did not support the validity of the new model. CONCLUSIONS: The factor structure of the MoCA in early PD was not consistent with that of previous research. This may reflect higher cognitive performance and differing demographics in our sample. The results do not support a clear, clinically relevant factor structure in an early PD group, suggesting that the MoCA should be followed with detailed assessment to obtain domain-specific cognitive profiles.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".