Psychometric Properties of the Montreal Cognitive Assessment (MoCA) in Parkinson's Disease Patients in Isfahan
Bibliographic record
Abstract
Background: The montreal cognitive assessment (MoCA) is a screening tool for evaluating mild cognitive impairments. The purpose of this research was to study the psychometric properties of the MoCA in Parkinson's disease patients in Isfahan, Iran. Methods: Thirty-five patients with Parkinson’s disease who satisfied all the inclusion criteria were referred to the researchers by a neurologist. They answered the MoCA, and the mini mental status examination (mmse) and demographic questionnaires. In addition, 40 healthy subjects with all the inclusion criteria except Parkinson's disease were randomly selected. They also completed the scales. Cronbach's alpha, Pearson correlation, and discriminant analysis were respectively used for computing reliability, concurrent validity, and diagnostic validity of the test. Findings: A Cronbach's alpha of 0.77, concurrent validity of 0.79, sensitivity of 0.85 and specificity of 0.90 were found. ROC table revealed 24 as the best cut-off point for MoCA. Conclusion: MoCA can be a valid and reliable instrument for assessing cognitive deficits in Iranian Parkinson's disease patients.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".