Use of the Montreal Cognitive Assessment and Alzheimer's Disease‐8 as cognitive screening measures in Parkinson's disease
Bibliographic record
Abstract
OBJECTIVE: To examine the sensitivity and specificity of the Montreal Cognitive Assessment (MoCA), a brief cognitive screening measure previously validated for use in Parkinson's disease (PD), and Alzheimer's Disease-8 (AD8), an eight-item informant report used to screen for dementia, but not yet validated for use in PD, to identify cognitive impairment in a sample of 111 patients with PD. METHODS: Cognitive impairment was determined based on a battery of neuropsychological measures, excluding the MoCA and AD8. Classification rates of both the MoCA and AD8 in identifying cognitive impairment were examined using logistic regression and receiver operator characteristic (ROC) analysis. Optimal cutoff scores were determined to maximize sensitivity and specificity. RESULTS: The MoCA correctly classified 78.4% of participants (p < 0.001), and ROC analysis yielded an area under the curve (AUC) of 0.82. A MoCA cutoff score of <25 yielded optimal sensitivity (0.77) and specificity (0.79) for identifying PD patients with cognitive impairment. Similar analyses for the AD8 were statistically nonsignificant, although the classification rate was 70.5%, with an AUC of 0.50. CONCLUSIONS: These results provide additional support for the MoCA, but not the AD8, in identifying cognitive impairment in patients with PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.021 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".