The Montreal Cognitive Assessment: Is It Suitable for Identifying Mild Cognitive Impairment in Parkinson's Disease?
Bibliographic record
Abstract
BACKGROUND: Administering an abbreviated global cognitive test, such as the Montreal Cognitive Assessment (MoCA), is necessary for the recommended first-level diagnostic criteria for mild cognitive impairment (MCI) in Parkinson's disease (PD). Level II requires administering cognitive functioning neuropsychological tests. The MoCA's suitability for identifying PD-MCI is questionable and, despite the importance of cognitive deficits reflected through daily functioning in identifying PD-MCI, knowledge about it is scarce. OBJECTIVES: To explore neuropsychological test scores of patients with PD who were categorized based on their MoCA scores and to analyze correlations between this categorization and patients' self-reports about daily functional-related cognitive abilities. METHODS: A total of 78 patients aged 42 to 78 years participated: 46 with low MoCA scores (22-25) and 32 with high MoCA scores (26-30). Medical assessments and level II neuropsychological assessment tools were administered along with standardized self-report questionnaires about daily functioning that reflects patients' cognitive abilities. RESULTS: A high percentage of the low MoCA group obtained neuropsychological test scores within the normal range; a notable number in the high MoCA group were identified with MCI-level scores on various neuropsychological tests. Suspected PD-MCI according to the level I criteria did not correspond well with the level II criteria. Positive correlations were found among the 3 self-report questionnaires. CONCLUSIONS: These results support the ongoing discussion of the complexity of capturing PD-MCI. Considering the neuropsychological tests results, assessments that reflect cognitive encounters in real life daily confrontations are warranted among people diagnosed with PD who are at risk for cognitive decline.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".