O4‐10‐01: Item response theory analysis of the montreal cognitive assessment from the Alzheimer's disease neuroimaging initiative
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) samples a broad range of cognitive abilities and is sensitive to Mild Cognitive Impairment (MCI) and Alzheimer's dementia (AD). We previously presented an item response theory analysis (IRT) of the MoCA, demonstrating its desirable psychometric properties as a test of global cognitive function in an ADRC setting (N=275). Here, we replicate those findings using a larger sample from ADNI, further investigating whether certain items perform differently by sex, education, or diagnosis. 1,199 participants (age = 73.0 ± 7.0, range = 55.0, 89.6; 45.9% Female; 92.3% White; years of education = 16.2 ± 2.7) completed the MoCA in the ADNI study, and were diagnosed as Normal Control (29.4%), MCI (57.2%), or AD (13.4%) by consensus. We estimated a two-parameter logistic graded response model using MoCA item-level data. Differential item functioning (DIF) was examined using the program IRTLRDIF. IRT analysis revealed that most MoCA items have high factor loadings (standardized [N(0,1)] r's>0.6), suggesting most are correlated well with the latent trait (Fig. A, y-axis), and have a desirably broad spread in item difficulty (Fig. A, x-axis). DIF by sex indicated that men were more likely to answer Serial 7, Trails B, and Watch items correctly across all levels of cognitive ability (Fig B), while women were more likely to answer Numbers and Face-Delayed Recall (at higher levels of ability) correctly. DIF by education was significant for the Fluency, Abstraction, Cube, Hand, and Year items; individuals with >12 years of education were more likely to provide correct responses on these items controlling for cognitive ability (Fig. C). DIF by diagnosis showed that Normal Controls provided correct responses to orientation items (i.e. Date, Day, Place) than MCI/AD participants regardless of cognitive ability (Fig. D). Figure
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".