A-10 MoCA-22: Internal Consistency and Convergent Validity of The MoCA’s Auditory Items
Bibliographic record
Abstract
Abstract Objective: The auditory items of the Montreal Cognitive Assessment (MoCA-22) have been packaged for use with visually impaired individuals or via telehealth. The goal of study is to evaluate the convergent and discriminant validity of the MoCA-22 with cognition, behavioral-psychological symptoms, and instrumental activities of daily living in order to expand the clinimetric foundation of the MoCA-22. Method: From the National Alzheimer’s Coordinating Center database, we extracted a diverse group (n = 11,284; mean age = 69.2 ± 10.0 years; 57.6% female; 77.9% White; education level 15.9 ± 3.0 years;) who completed the MoCA during their first visit to an ADRC. Confirmatory factor analysis, Cronbach’s alpha, and McDonald’s omega were used to evaluate the fit and reliability of MoCA-22. Convergent/discriminant validity was evaluated via Spearman’s rho with factors derived from Version 3.0 of the Uniform Data Set’s Neuropsychological Battery (UDS3-NB), Functional Activities Questionnaire (FAQ), Geriatric Depression Scale (GDS-15), and collateral-report version of Neuropsychiatric Inventory (NPI-Q). Results: A single-factor adequately fit the data, and reliability estimates ranged from 0.81 to 0.87, suggesting the MoCA-22 can be interpreted individually. MoCA-22 showed convergent validity, with the strongest correlations with the UDS3-NB’s “general cognition” and “executive” factors (rhos = 0.67 and 0.69). Discriminant validity was demonstrated with its weakest correlations being with behavioral-psychological symptoms (self-reported depression rho = −0.23; collateral-reported neuropsychiatric symptoms rho = −0.42). Conclusion: The current study further supports use of the MoCA-22 when the evaluation context limits use of the MoCA’s visual items.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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) | 0.002 | 0.001 |
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".