Robust demographically-adjusted normative data for the Montreal Cognitive Assessment (MoCA): Results from the systolic blood pressure intervention trial
Bibliographic record
Abstract
To generate robust, demographically-adjusted regression-based norms for the Montreal Cognitive Assessment (MoCA) using a large sample of diverse older US adults. Baseline MoCA scores were examined for participants in the Systolic Blood Pressure Intervention Trial (SPRINT). A robust, cognitively-normal sample was drawn from individuals not subsequently adjudicated with cognitive impairment through 4 years of follow-up. Multivariable Beta-Binomial regression was used to model the association of demographic variables with MoCA performance and to create demographically-stratified normative tables. Participants' (N = 5,338) mean age was 66.9 ± 8.8 years, with 35.7% female, 63.1% White, 27.4% Black, 9.5% Hispanic, and 44.5% with a college or graduate education. A large proportion scored below published MoCA cutoffs: 61.4% scored below 26 and 29.2% scored below 23. A disproportionate number falling below these cutoffs were Black, Hispanic, did not graduate from college, or were ≥75 years of age. Multivariable modeling identified education, race/ethnicity, age, and sex as significant predictors of MoCA scores (p<.001), with the best fitting model explaining 24.4% of the variance. Model-based predictions of median MoCA scores were generally 1 to 2 points lower for Black and Hispanic participants across combinations of age, sex, and education. Demographically-stratified norm-tables based on regression modeling are provided to facilitate clinical use, along with our raw data. By using regression-based strategies that more fully account for demographic variables, we provide robust, demographically-adjusted metrics to improve cognitive screening with the MoCA in diverse older adults.
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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 | 0.021 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".