Comparing and linking the Mini-Mental State Examination and Montreal Cognitive Assessment in the Amsterdam Dementia Cohort
Bibliographic record
Abstract
OBJECTIVES: We aimed to compare and link the total scores of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), two common global cognitive screeners. METHODS: 2,325 memory clinic patients (63.2 ± 8.6 years; 43% female) with a variety of diagnoses, including subjective cognitive decline, mild cognitive impairment, and dementia due to various etiologies completed the MMSE and MoCA concurrently. We described both screeners, including at the item level. Then, using linear regressions, we investigated how age, sex, education, and diagnosis affected total scores on both instruments. Next, in linear mixed models, we treated the two screeners as repeated measures and analyzed the influence of these characteristics on the relationship between the instruments' total scores. Finally, we linked total scores using equipercentile equating, accounting for relevant patient characteristics. RESULTS: MMSE scores (mean ± standard deviation: 25.0 ± 4.6) were higher than MoCA scores (21.2 ± 5.4), and MMSE items generally showed less variation than MoCA items. Both instruments' scores were individually influenced by age, sex, education, and diagnosis. The relationship between the screeners was moderated by age (estimate = -0.01, 95% confidence interval = [-0.03, -0.00]), education (0.14 [0.10, 0.18]), and diagnosis. These were accounted for when producing crosswalk tables based on equipercentile equating. CONCLUSIONS: Accounting for the influence of patient characteristics, we created crosswalk tables to convert MMSE scores to MoCA scores, and vice versa. These tables may facilitate collaboration between clinicians and researchers and could allow larger, pooled analyses of global cognitive functioning in older adults.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".