Agreement and conversion formula between mini-mental state examination and montreal cognitive assessment in an outpatient sample
Bibliographic record
Abstract
AIM: To explore the agreement between the mini-mental state examination (MMSE) and montreal cognitive assessment (MoCA) within community dwelling older patients attending an old age psychiatry service and to derive and test a conversion formula between the two scales. METHODS: Prospective study of consecutive patients attending outpatient services. Both tests were administered by the same researcher on the same day in random order. RESULTS: The total sample (n = 135) was randomly divided into two groups. One to derive a conversion rule (n = 70), and a second (n = 65) in which this rule was tested. The agreement (Pearson's r) of MMSE and MoCA 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 MoCA scores were converted to MMSE scores according to a conversion rule from the first sample which achieved agreement with the original MMSE scores of 0.89 (Pearson's r, P < 0.001) and CCC of 0.88 (95%CI: 0.82-0.92). CONCLUSION: Although the two scales overlap considerably, the agreement is modest. The conversion rule derived herein demonstrated promising accuracy and warrants further testing in other populations.
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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 | 0.031 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".