Agreement and equation between Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in an old age psychiatry outpatient clinic population
Bibliographic record
Abstract
Introduction Both MMSE and MoCA are two widely used cognitive screening test. Comparison of the two tests has been done in specific populations (Parkinson) but not in general elderly psychiatric populations. In research, equating methodologies has been used to compare results among studies that use different scales, which measure the same construct. Aims To explore their level of agreement within a particular clinical setting. Objectives (a) To find MoCA and MMSE agreement. (b) To derive a conversion formula between the two scales and test it in a random population of similar setting. Methods Prospective study of consecutive community dwelling older patients who attend outpatient clinic or day hospital. Both tests were administered from the same researcher the same day in random order. Results The total sample (n = 135) was randomly divided in two. One from where the equating rule derived (n = 70) and a second (n = 65) in which the derived conversion was tested. Agreement of the two scales (Pearson's r) was 0.86 (P < 0.001), and Lin's Concordance Correlation Coefficient (CCC) was 0.57 (95% CI 0.45–0.66). In the second sample, we convert the MoCA scores to MMSE scores according to equating rule from the first sample and after we examined the agreement between the converted MMSE scores and the originals. The Pearson's r was 0.89 (n = 65, P < 0.001) and the CCC 0.88 (95% CI 0.82–0.92). Conclusions Although the two scales overlap considerably, the agreement is modest. The conversion rule derived showed promising accuracy in this population but need further testing in other populations. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".