Comparison of the use of screening tools for evaluating cognitive impairment in patients with Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Screening tests have been used for cognitive deficits in Parkinson's disease (PD). OBJECTIVE: This study compared the Montreal Cognitive Assessment (MoCA) test, the Mini-Mental State Examination (MMSE) and the clock drawing test for this purpose. METHODS: A total of 50 patients with PD were selected, 41 (82%) were diagnosed with dementia by the criteria of the Movement Disorder Society. The test Scales for Outcomes in Parkinson's Disease-Cognition (SCOPA-Cog) was used as the gold standard in comparison with the screening tests. RESULTS: The MoCA test (AUC=0.906) had a sensitivity of 87.80% and specificity of 88.89%. When the MMSE was associated with the clock drawing test (AUC=0.936), it had a specificity of 66.67% and sensitivity of up to 97.56%. CONCLUSION: The study suggests that the MoCA test can be a good screening test in PD. However, MMSE associated with the clock drawing test may be more effective than the MoCA test.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".