Psychometric Properties of the Montreal Cognitive Assessment (MoCA): An Analysis Using the Rasch Model
Bibliographic record
Abstract
In the present study we analyzed the psychometric characteristics of the MoCA (Portuguese version) using the Rasch model for dichotomous items. The total sample comprised of 897 participants distributed between two main subgroups: (I) healthy group that was comprised of 650 cognitively healthy community dwellers and (II) clinical group that was comprised of 90 patients with Mild Cognitive Impairment, 90 patients with Alzheimer's disease, 33 patients with frontotemporal dementia, and 34 patients with vascular dementia recruited at a reference dementia clinic. All patients were investigated through a comprehensive neuropsychological assessment, laboratory tests essential to exclude a reversible form of dementia, imaging studies (CT or MRI and SPECT or FDG-PET), Apolipoprotein E allele genotyping and CSF biomarker (Aβ42,Tau, and P-tau) analyses. The clinical diagnosis was established through the consensus of a multidisciplinary team, based on international criteria. The results demonstrated an overall good fit of both items and the person's values, a high variability on cognitive performance level, and a good quality of the measurements. The MoCA scores also demonstrated adequate discriminant validity, with high diagnostic value. DIF analyses indicated the generalized validity of the MoCA scores. In conclusion, the results of this study show the overall psychometric adequacy of the MoCA and verify the discriminant and generalized validity of the obtained results.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".