Multicenter Validation of an <scp>MMSE</scp> ‐Mo <scp>CA</scp> Conversion Table
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence points to the superiority of the MoCA over the MMSE as a cognitive screening tool. To facilitate the transition from the MMSE to the MoCA in clinical and research settings, authors have developed MMSE-MoCA conversion tables. However, it is unknown whether a conversion table generated from Alzheimer's disease (AD) patients would apply to patients with other dementia subtypes like vascular dementia or frontotemporal dementia. Furthermore, the reliability and accuracy of MMSE-MoCA conversion tables has not been properly evaluated. METHOD: We retrospectively examined the MMSE-MoCA relationship in a large multicenter sample gathered from 3 Memory Clinics in Quebec, Canada (1492 patients). We produced an MMSE-MoCA conversion table using the equi-percentile method with log-linear smoothing. We then cross-validated our conversion table with the ADNI dataset (1202 patients) and evaluated its accuracy for future predictions. RESULTS: The MMSE-MoCA conversion table is consistent with previously published tables and has an intra-class correlation of 0.633 with the ADNI sample. However, we found that the MMSE-MoCA relationship is significantly modified by diagnosis (P < .01), with dementia subtypes associated with a dysexecutive syndrome showing a trend towards higher MMSE than other dementia syndromes for a given MoCA score. The large width of 95% confidence interval (CI) for a new prediction suggests questionable reliability for clinical use. CONCLUSION: In this study, we validated a conversion table between MMSE and MoCA using a large multicenter sample. Our results suggest caution in interpreting the tables in heterogeneous clinical populations, as the MMSE-MoCA relationship may be different across dementia subtypes.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".