A-09Relationship of the Montreal Cognitive Assessment (MoCA) to Everyday Impairments in Parkinson's Disease
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a widely used cognitive screening tool in Parkinson's disease (PD) and is sensitive to PD-mild cognitive impairment (MCI) and dementia. The relationship of the MoCA to difficulties in activities of daily living in PD more particularly has yet to be explored. Objective: The aim of this study is to explore how caregiver reported everyday impairments correlated with associated cognitive domains from the MoCA as well as explore the sensitivity and specificity of this instrument to daily difficulties in a PD sample. It was hypothesized that MoCA subscales (i.e. Language, Memory, Visual, and Executive) would correlate with respective everyday impairments as measured by the Everyday Cognition (ECog) questionnaire and would be sensitive to everyday impairment observed in patients with PD. Method: A convenience sample of 49 patients with PD (67% male; 96% Caucasian; 41% Hoehn & Yahr Stage III) were administered the MoCA, while an informant (e.g., significant other or caregiver) completed the ECog. Results: A weak relationship between the MoCA total score and several domains of everyday cognitive problems as perceived by caregivers was revealed. The MoCA memory subscale was most strongly related to caregiver reported daily difficulties. The sensitivity of the MoCA total score to everyday cognitive problems was modest with poor specificity. Conclusion: Results indicated the MoCA has only modest relationship with more subtle everyday problems. Implications and future directions for cognitive assessment in PD are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".