Using the Montreal cognitive assessment to identify individuals with subtle cognitive decline.
Bibliographic record
Abstract
OBJECTIVE: Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In this study, we used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline. METHOD: -modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression. RESULTS: A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others. CONCLUSIONS: We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".