55 Tracking Cognitive Change in Huntington’s Disease with the Mini Mental State Exam and the Montreal Cognitive Assessment
Bibliographic record
Abstract
Objective: To assess the utility of the Mini Mental State Exam (MMSE) and Montreal Cognitive Assessment (MoCA) for tracking cognitive changes Huntington’s Disease. Participants and Methods: Currently, the most frequently used brief assessment of global cognitive functioning is the MMSE. Although the MMSE is helpful for distinguishing individuals without significant cognitive impairment from those with dementia, it is not particularly sensitive to more subtle cognitive deficits. The MoCA is another brief cognitive screening tool that has been shown to be more sensitive to mild impairment and may have greater usefulness in subcortical dementias because of its more extensive assessment of executive function. Although the MoCA appears to have high sensitivity and specificity in a variety of neurological populations, there is currently little known about its efficacy in tracking cognitive decline in individuals with HD. We used a mixed effects model to analyze MMSE and MoCA scores collected prospectively during 5 years of follow-up for 163 patients with HD seen at one academic HDSA Center of Excellence. Baseline mean age for the HD cohort was 51.35 years, mean education 14.46 years, and a mean CAG repeat length 43.95. Mean follow-up time was 3.33 years. Results: Mean MMSE and MoCA scores at baseline were 25.13 (SD=1.66) and 22.76 (SD=3.70) respectively. At baseline, age and gender were not associated with MMSE and MoCA scores, while years of education were. Neither age nor gender predicted rate of decline for the MoCA while years of education predicted rate of decline for the MMSE. For the MMSE, each year of education predicted on average 0.51 points higher score at enrollment; for the MoCA, each year of education predicted on average 0.79 points higher score at enrollment. The mean rates of decline on the MMSE was 0.48 points per year (p<.001) while that on the MoCA was only 0.31 points annually (p<.001) in the first five years of observation. Conclusions: The MMSE and MoCA decline significantly over time in an unselected HD population. The smaller rate of decline in the MoCA may be due, in part, to the greater variability in baseline, MoCA (SD=3.70) vs MMSE (SD=1.66) scores in our HD cohort. Unlike cortical dementias, such as Alzheimer’s disease (AD), where declines of 2-3 points per year have been described for the MMSE and MoCA, much lower annual rates of decline have been reported in subcortical dementias such as Parkinson’s disease. To our knowledge, this is the first report of rate of cognitive decline on the MMSE and MoCA in HD: such information is vital for adequately preparing patients and families for future needs, in addition to planning for interventional/treatment trials in HD.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".