Converting MMSE to MoCA and MoCA 5‐minute protocol in an educationally heterogeneous sample with stroke or transient ischemic attack
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is psychometrically superior over the Mini-mental State Examination (MMSE) for cognitive screening in stroke or transient ischemic attack (TIA). It is free for clinical and research use. The objective of this study is to convert scores from the MMSE to MoCA and MoCA-5-minute protocol (MoCA-5 min) and to examine the ability of the converted scores in detecting cognitive impairment after stroke or TIA. METHODS: A total of 904 patients were randomly divided into training (n = 623) and validation (n = 281) samples matched for demography and cognition. MMSE scores were converted to MoCA and MoCA-5 min using (1) equipercentile method with log-linear smoothing and (2) Poisson regression adjusting for age and education. Receiver operating characteristics curve analysis was used to examine the ability of the converted scores in differentiating patients with cognitive impairment. RESULTS: The mean education was 5.8 (SD = 4.6; ranged 0-20) years. The entire spectrum of MMSE scores was converted to MoCA and MoCA-5 min using equipercentile method. Relationship between MMSE and MoCA scores was confounded by age and education, and a conversion equation with adjustment for age and education was derived. In the validation sample, the converted scores differentiated cognitively impaired patients with area under receiver operating characteristics curve 0.826 to 0.859. CONCLUSION: We provided 2 methods to convert scores from the MMSE to MoCA and MoCA-5 min based on a large sample of patients with stroke or TIA having a wide range of education and cognitive levels. The converted scores differentiated patients with cognitive impairment after stroke or TIA with high accuracy.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".