P3‐453: NORMATIVE DATA ON THE MONTREAL COGNITIVE ASSESSMENT FOR AN OLDER AMERICAN POPULATION
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is an evaluation tool used for initial assessment of cognition. A score of ≤26 is indicative of below normal cognition. The tool has been introduced to assess for Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD). The normative sample for the MoCA was primarily Canadian but is now used for US populations despite differences in reading-level between countries. Also, normative data is not yet available for populations with at increased risk of MCI, such as ethnic minority elderly and those with lower educational attainment. This may result in an incorrect clinical classification. We performed this analysis to generate American population-based normative data for the MoCA. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, national study to develop clinical, imaging, and biochemical biomarkers for early detection and monitoring of AD. At the time of analysis, 1185 participants completed an initial visit which included the MoCA. From the ADNI data, 488 were cognitively normal with 91.2% White, 58.4% female. The sample's mean years of education was 16.75±2.42 with a mean age of 72±6.17. The raw mean for MoCA score was 25.99±2.48 with women scoring higher (26.36±2.42) than men (25.51±2.52). No differences between racial categories were observed. In a multivariate regression model, age, years of education, and sex accounted for 33% of the variance in MoCA score generating the following normative regression equation for this sample: ÝMoCA=23.31-0.11A+1.141S+0.39E [S=sex(1=men,2=women), A=age, E=education]. Normative data were stratified by overlapping age bands, sex, and educational attainment for clinical reference. MoCA's raw means for the ADNI sample were significantly different from the reported 27.4 average scores in the initial sample. The mean score for males in ADNI fell below the suggested cutoffs. Also, ADNI participants had higher educational attainment compared to the US population as over half of the ADNI sample completed ≥16 years of education compared to the reported 27% of Americans aged ≥65. We conclude that these differences may influence MoCA scores in addition to the effects of psychological, behavioral, and cultural variables on cognition. Future studies should consider a US population-based data to derive cutoff scores.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".