The Montreal Cognitive Assessment and the Mini-Mental State Examination as Screening Instruments for Cognitive Impairment: Item Analyses and Threshold Scores
Bibliographic record
Abstract
AIMS: To perform an item analysis of the Montreal Cognitive Assessment (MoCA) versus the Mini-Mental State Examination (MMSE) in the prediction of cognitive impairment, and to examine the characteristics of different MoCA threshold scores. METHODS: 135 subjects enrolled in a longitudinal clinicopathologic study were administered the MoCA by a single physician and the MMSE by a trained research assistant. Subjects were classified as cognitively impaired or cognitively normal based on independent neuropsychological testing. RESULTS: 89 subjects were found to be cognitively normal, and 46 cognitively impaired (20 with dementia, 26 with mild cognitive impairment). The MoCA was superior in both sensitivity and specificity to the MMSE, although not all MoCA tasks were of equal predictive value. A MoCA threshold score of 26 had a sensitivity of 98% and a specificity of 52% in this population. In a population with a 20% prevalence of cognitive impairment, a threshold of 24 was optimal (negative predictive value 96%, positive predictive value 47%). CONCLUSION: This analysis suggests the potential for creating an abbreviated MoCA. For screening in primary care, the MoCA threshold of 26 appears optimal. For testing in a memory disorders clinic, a lower threshold has better predictive value.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".