A-02 Comparing Rate of Change in MoCA and MMSE Scores Over Time in an MCI and AD sample
Bibliographic record
Abstract
Abstract Objective The Montreal Cognitive Assessment (MoCA) and Mini-Mental Status Exam (MMSE) are used for tracking cognitive change in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), despite limited empirical support for this purpose. This study compared longitudinal change in MMSE and MoCA scores to investigate their applicability for tracking cognitive change. Method Inclusion criteria were: diagnosis of MCI or AD (CDR global = .5) by consensus conference, administration of the MoCA and MMSE across ≥3 visits, and no reversion to normal (n = 59; Mage = 70.81; Meducation = 14.97; 56% male; 76.3% Caucasian; 80% MCI at baseline). Testing sessions occurred ~ 12 months apart (M = 12.59, SD = 3.43, range 5–28 months). Change in MMSE and MoCA scores was modeled using multilevel regression. A 95% bootstrap confidence interval (BCI) for the slopes of both tests was computed and used to evaluate whether the tests measured significantly different change. Results Controlling for age and education, the MoCA demonstrated significantly more change over time (95% BCI [−0.06, −0.02]; MoCA Visit 1 M = 24.00, Visit 4 M = 21.88) than the MMSE (95% BCI [−0.03, 0.01]; MMSE Visit 1 M = 27.83, Visit 4 M = 27.50). MoCA scores significantly declined over the study period (but did not exceed the reliable change index), while MMSE scores did not. Conclusions The MMSE did not show significant change over time, while the MoCA did in this heavily MCI sample. Although statistically significant, clinical significance of change in the MoCA is unclear. Increasing MoCA use calls for additional research to understand what constitutes a clinically significant change and whether it is appropriate for tracking cognitive trajectories.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".