Conversion Between the Mini-Mental State Examination and the Montreal Cognitive Assessment for Patients With Different Forms of Dementia
Bibliographic record
Abstract
OBJECTIVES: The Mini-Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are 2 frequently used brief cognitive screening tasks. Here, we provide a conversion method from MMSE to MoCA for patients with Alzheimer's dementia, frontotemporal dementia, and Parkinson dementia/Lewy body dementia, as well as for patients with dementia and with or without previous stroke. This conversion is needed as everyday clinical practice varies in their use of the 2 scales, which makes comparisons between studies, meta-analysis, and patient cohorts difficult. DESIGN: Observational cohort study. SETTING AND PARTICIPANTS: A total of 387 patients with recently diagnosed dementia in memory clinics from the Swedish registry for cognitive/dementia disorders (SveDem) from 2007 to 2018. METHODS: Overall, 387 patients of the Swedish registry for cognitive/dementia disorders with both MMSE and MoCA scores were evaluated. An equipercentile equating method was used to convert MMSE to MoCA scores in the different patient populations. Furthermore, receiver operating curves were used to examine whether MMSE or MoCA scores can distinguish between patients with different dementia types. RESULTS: MMSE scores were converted to MoCA scores for all dementia types and depicted in a conversion table. Results show that the equipercentile equating method and log-linear smoothing allow the creation of a conversion table in which for each test score of the MMSE, the equivalent score of the MoCA for each investigated group can be looked up (and vice-versa). CONCLUSIONS AND IMPLICATIONS: This study reports a reliable and easy conversion for transforming MMSE to MoCA scores (and vice-versa) in patients with Alzheimer's dementia, frontotemporal dementia, Parkinson dementia or Lewy body dementia, as well as patients with dementia with and without previous stroke.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".