Validation of conversion between mini–mental state examination and montreal cognitive assessment
Bibliographic record
Abstract
INTRODUCTION: Harmonizing data across cohorts is important for validating findings or combining data in meta-analyses. We replicate and validate a previous conversion of MoCA to MMSE in PD. METHODS: We used five studies with 1,161 PD individuals and 2,091 observations measured with both the MoCA and MMSE. We compared a previously published conversion table using equipercentile equating with log-linear smoothing to our internally derived scores. RESULTS: Both conversions found good agreement within and across the studies when comparing true and converted MMSE (mean difference: 0.05; standard deviation: 1.84; median difference: 0; interquartile range: -1 to 1, using internal conversion). CONCLUSIONS: These results show that one can get a reliable and valid conversion between two commonly used measures of cognition in PD studies. These approaches need to be applied to other scales and domains to enable large-scale collaborative analyses across multiple PD cohorts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".