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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".