Longitudinal changes in MoCA performances in patients with mild cognitive impairment and small vessel disease. Results from the VMCI-Tuscany Study
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a cognitive screening test largely employed in vascular cognitive impairment, but there are no data about MoCA longitudinal changes in patients with cerebral small vessel disease (SVD). We aimed to describe changes in MoCA performance in patients with mild cognitive impairment (MCI) and SVD during a 2-year follow-up, and to evaluate their association with transition to major neurocognitive disorder (NCD). Within the prospective observational VMCI-Tuscany Study, patients with MCI and SVD underwent a comprehensive clinical, neuropsychological, and functional evaluation at baseline, and after 1 and 2 years. Among the 138 patients (mean age 74.4 ± 6.9 years; males: 57%) who completed the study follow-up, 44 (32%) received a major NCD diagnosis. Baseline MoCA scores (mean±SD) were lower in major NCD patients (20.5 ± 5) than in reverter/stable MCI (22.2 ± 4.3), and the difference approached the statistical threshold of significance (p=.051). The total cohort presented a decrease in MoCA score (mean±SD) of -1.3 ± 4.2 points (-2.6 ± 4.7 in major NCD patients, -0.7 ± 3.9 in reverter/stable MCI). A multivariate logistic model on the predictors of transition from MCI to major NCD, showed MoCA approaching the statistical significance (OR=1.09, 95% CI=1.00–1.19, p=.049). In our sample of MCI patients with SVD, longitudinal changes in MoCA performances were consistent with an expected more pronounced deterioration in patients who received a diagnosis of major NCD. MoCA sensitivity to change and predictive utility need to be further explored in VCI studies based on larger samples and longer follow-up periods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".