Bridging cognitive screening tests in neurologic disorders: A crosswalk between the short Montreal Cognitive Assessment and Mini‐Mental State Examination
Bibliographic record
Abstract
INTRODUCTION: To provide a crosswalk between the recently proposed short Montreal Cognitive Assessment (s-MoCA) and Mini-Mental State Examination (MMSE) within a clinical cohort. METHODS: A total of 791 participants, with and without neurologic conditions, received both the MMSE and the MoCA at the same visit. s-MoCA scores were calculated and equipercentile equating was used to create a crosswalk between the s-MoCA and MMSE. RESULTS: As expected, s-MoCA scores were highly correlated (Pearson r = 0.82, P < .001) with MMSE scores. s-MoCA scores correctly classified 85% of healthy older adults and 91% of individuals with neurologic conditions that impair cognition. In addition, we provide an easy to use table that enables the conversion of s-MoCA score to MMSE score. DISCUSSION: The s-MoCA is quick to administer, provides high sensitivity and specificity for cognitive impairment, and now can be compared directly with the MMSE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".