Equating conversion norms for the Mini-Mental State Examination and Montreal Cognitive Assessment in healthy subjects and patients with neurodegenerative disorders
Bibliographic record
Abstract
The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are globally recognized as validated cognitive screening tests widely used. The present study attempted to provide conversion tables from the MMSE to the MoCA and vice versa, deriving them from a large population of healthy older adults and a representative clinical sample of subjects with different types of cognitive decline within the spectrum of Alzheimer's (AD) and Parkinson's (PD) diseases. A total of 1423 Italian participants, including healthy adults (n = 1203), individuals with AD (n = 93), and with PD (n = 127) were assessed using the MMSE and MoCA. Conversion tables were developed using log-linear smoothing equipercentile equating (LSEE). The reliability of the conversion was assessed through the Root Mean Square Error (RMSE) in a train-test approach confirmed in the whole sample. The findings demonstrated that the LSEE method enables the development of conversion tables allowing users to identify the corresponding MoCA score for each MMSE score within the studied groups, and vice versa. The estimation error RMSE was 1.8, 2.9, and 3.2 for the conversion of MoCA from MMSE and 1.2, 2.3, and 2.2 for the conversion of MMSE from MoCA in healthy subjects, AD, and PD, respectively. The reliability of the conversion is higher in healthy subjects and for higher values of MoCA and MMSE. Results report easy-to-use conversion norms for transforming raw MMSE score to MoCA and vice versa, highlighting areas were the conversion has a strong or low reliability depending on the score range. • Screening tests as useful tools for rapid assessment of overall cognitive function • Conversion MMSE/MoCA tables for Italian healthy and neurodegenerative aging • Availability of normative data on the characteristics of disease specific population • Conversion tables address inter-study variability between MMSE and MoCA scores. • Need of longitudinal cognitive monitoring if using different screening tools
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".