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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".