P2‐285: Construct validity of MoCA subscales in a population‐based sample
Bibliographic record
Abstract
There is increased interest in efficient methods for early detection of cognitive signs of AD in clinic and community samples. The Montreal Cognitive Assessment (MoCA) is a cognitive screening instrument that is gaining in popularity for this purpose. Yet, the properties of the instrument have not been thoroughly investigated. We evaluated the performance of the MoCA in comparison to well-established cognitive tests that are recognized as being sensitive to early cognitive signs of AD. PREPARE is an ongoing study to prescreen participants for AD prevention trials through community outreach. Thus far, 412 participants completed screening measures including a brief lifestyle questionnaire, health/medical history, a blood draw, and a brief neuropsychological battery (MoCA, the CERAD Word List Learning Task (WLM), the Trail Making Test Part B (Trails-B), and the ADCS Cognitive Function Screening Instrument (ADCScog)). MoCA subscales organized by logical cognitive domains were created by taking unweighted sums of test items forming the following subscales: memory, executive function, attention, orientation, language, and visuospatial function. Domain totals were converted to z-scores. Multiple linear regression models were used to evaluate associations between MoCA domains and normalized WLM, Trails B, and ADCScog scores, each in separate models adjusted for age, sex, and education. The sample was mostly female (74%), Caucasian (80% Caucasian; 18% AfrAmer), and highly educated (mean 16.4, standard deviation (SD) 2.3). The average MoCA score was 26.6 (SD 2.9). The MoCA memory score was the strongest predictor of WLM (0.34, 95% CI 0.026–0.43) with a one-third standard unit increase on WLM score for every unit increase in memory score. Memory was also the strongest predictor of ADCScog score (higher scores indicate impairment) (- 0.18, CI - 0.27- - 0.08). MoCA executive function was the strongest predictor of Trails-B, with each unit increase in executive function corresponding to almost a one third standard unit decrease on Trails B (- 0.29, CI - 0.40- - 0.19). Simple MoCA subscales correspond with well-established cognitive tests (WLM, Trails-B, and ADCScog). This study helps establish the construct validity of the MoCA as a cognitive screening instrument capable of detecting key cognitive domains affected in MCI and early AD in a community sample.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".