Montreal Cognitive Assessment vs the Mini-Mental State Examination as a Screening Tool for Patients With Genetic Frontotemporal Dementia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: With upcoming clinical trials targeting preclinical stages of genetic frontotemporal dementia (FTD), early detection through cognitive screening is crucial. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) have potential as screening instruments for early-stage genetic FTD. However, no comparative evaluation has been performed. We aimed to compare MMSE and MoCA performance among presymptomatic, prodromal, and symptomatic pathogenic variant carriers to analyze which screening test has superior discriminative abilities. METHODS: We used cross-sectional and longitudinal data from 2 longitudinal genetic FTD cohort studies in the Netherlands and the United Kingdom, collected between 2021 and 2024. Participants were either presymptomatic, prodromal, or symptomatic pathogenic variant carriers or healthy controls (first-degree family members without pathogenic variants for FTD). Grouping was based on the global CDR-plus-NACC-FTLD score. Participants were assessed with both MoCA and MMSE. Statistical analyses compared total and subscores between groups and evaluated predictive and classification accuracy of both tests. RESULTS: < 0.001] total scores differed significantly between groups, with controls (median MoCA 28.5, 95% CI 28.0-29.0; median MMSE 30, 95% CI 30.0-30.0) outperforming prodromal (median MoCA 26, 95% CI 23.0-27.0; median MMSE 29, 95% CI 27.5-29.5) and symptomatic (median MoCA 20.5, 95% CI 17.0-24.0; median MMSE 26, 95% CI 23.5-29.0) carriers. MoCA distinguished between presymptomatic carriers and controls (median MoCA 28, 95% CI 27.0-29.0), but MMSE did not. MoCA demonstrated superior discriminative ability compared with MMSE (MoCA area under the curve [AUC] = 0.87, 95% CI 0.81-0.94; MMSE AUC = 0.80, 95% CI 0.72-0.89). DISCUSSION: Its higher sensitivity and better discriminative power make MoCA a more valuable tool for cognitive screening in upcoming clinical trials targeting preclinical FTD. Future studies should aim for larger sample sizes from additional study centers.
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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 | 0.003 | 0.009 |
| 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) | 0.002 | 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".