Correcting the MoCA for Education: Effect on Sensitivity
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to quantify the impact of the suggested education correction on the sensitivity and specificity of the Montreal Cognitive Assessment (MoCA). METHOD: Twenty-five outpatients with dementia and 39 with amnestic mild cognitive impairment (aMCI) underwent a diagnostic evaluation, which included the MoCA. Thirty-seven healthy controls also completed the MoCA and psychiatric, medical, neurological, functional, and cognitive difficulties were ruled out. RESULTS: For the total MoCA score, unadjusted for education, a cut-off score of 26 yielded the best balance between sensitivity and specificity (80% and 89% respectively) in identifying cognitive impairment (people with either dementia or aMCI, versus controls). When applying the education correction, sensitivity decreased from 80% to 69% for a small specificity increase (89% to 92%). The cut-off score yielding the best balance between sensitivity and specificity for the education adjusted MoCA score fell to 25 (61% and 97%, respectively). CONCLUSIONS: Adjusting the MoCA total score for education had a detrimental effect on sensitivity with only a slight increase in specificity. Clinically, this loss in sensitivity can lead to an increased number of false negatives, as education level does not always correlate to premorbid intellectual function. Clinical judgment about premorbid status should guide interpretation. However, as this effect may be cohort specific, age and education corrected norms and cut-offs should be developed to help guide MoCA interpretation.
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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 | 0.071 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".