Reliable change indices for the Italian version of the Montreal Cognitive Assessment (MoCA) in non-demented Parkinson’s disease patients
Bibliographic record
Abstract
BACKGROUND: . The present study aimed at deriving regression-based reliable change indices (RCIs) for the Montreal Cognitive Assessment (MoCA) in an Italian cohort of non-demented Parkinson's disease (PD) patients. METHODS: N = 33 consecutive, non-demented PD patients were followed-up at a 5-to-8-month interval (M = 6.6; SD = 0.6) with the MoCA. Practice effects and test-retest reliability were assessed via dependent-sample t-tests and intra-class correlation (ICC) coefficients, respectively. RCIs were derived separately for raw and demographically adjusted MoCA scores according to a standardized regression-based approach by accounting for both baseline confounders (i.e., demographics, disease duration and Unified Parkinson's Disease Rating Scale scores) and retest interval. RESULTS: No practice effects were found (t(32) = 0.29; p = .778), with acceptable test-retest reliability being detected (ICC = 0.67). MoCA scores at T0 proved to be the only significant predictor of T1 MoCA performances within both the model addressing raw scores and that addressing adjusted scores (ps < 0.001). CONCLUSIONS: The present study provides Italian practitioners and researchers with regression-based RCIs for the MoCA in non-demented PD patients, which can be reliably adopted for retest interval ≥ 5 and ≤ 8 months without encountering any practice effect.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".