Examining the Effects of Formal Education Level on the Montreal Cognitive Assessment
Bibliographic record
Abstract
Background: Brief, global assessments such as the Montreal Cognitive Assessment (MoCA) are widely used in primary care for assessing cognition in older adults. Like other neuropsychological instruments, lower formal education can influence MoCA interpretation. Methods: Data from 2 large studies of cognitive aging were used—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC). Both use comprehensive examinations to determine cognitive status and have brain amyloid status for many participants. Mixed models were used to account for random variation due to data source. Results: Cognitively intact participants with lower education (≤12 years) were more likely than those with higher education (>12 years) to be classified as potentially impaired using the MoCA cutoff of <26 (P < .01). Backwards selection revealed 4 MoCA items significantly associated with education (cube copy, serial subtraction, phonemic fluency, abstraction). Subtracting these items scores yielded an alternative MoCA score with a maximum of 24 and a cutoff of ≤19 for classifying participants with mild cognitive impairment. Using the alternative MoCA score and cutoff, among cognitively intact participants, both education groups were similarly likely to be classified as potentially impaired (P > .67). Conclusions: The alternative MoCA score neutralized the effects of formal education. Although further research is needed, this alternative score offers a simple procedure for interpreting MoCAs administered to older adults with ≤12 years education. These educational effects also highlight that the MoCA is part of the assessment process—not a singular diagnostic test—and a comprehensive workup is necessary to accurately diagnose cognitive impairments.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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) | 0.007 | 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 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".