The MoCA
Bibliographic record
Abstract
OBJECTIVE: To establish the diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) when screening externally validated cognition in Parkinson disease (PD), by comparison with a PD-focused test (Scales for Outcomes in Parkinson disease-Cognition [SCOPA-COG]) and the standardized Mini-Mental State Examination (S-MMSE) as benchmarks. METHODS: A convenience sample of 114 patients with idiopathic PD and 47 healthy controls was examined in a movement disorders center. The 21 patients with dementia (PD-D) were diagnosed using Movement Disorders Society criteria, externally validated by detailed independent functional and neuropsychological tests. The 21 patients with mild cognitive impairment (PD-MCI) scored 1.5 SD or more below normative data in at least 2 measures in 1 of 4 cognitive domains. Other patients had normal cognition (PD-N). RESULTS: Primary outcomes using receiver operating characteristic (ROC) curve analyses showed that all 3 mental status tests produced excellent discrimination of PD-D from patients without dementia (area under the curve [AUC], 87%-91%) and PD-MCI from PD-N patients (AUC, 78%-90%), but the MoCA was generally better suited across both assessments. The optimal MoCA screening cutoffs were <21/30 for PD-D (sensitivity 81%; specificity 95%; negative predictive value [NPV] 92%) and <26/30 for PD-MCI (sensitivity 90%; specificity 75%; NPV 95%). Further support that the MoCA is at least equivalent to the SCOPA-COG, and superior to the S-MMSE, came from the simultaneous classification of the 3 PD patient groups (volumes under a 3-dimensional ROC surface, chance = 17%: MoCA 79%, confidence interval [CI] 70%-89%; SCOPA-COG 74%, CI 62%-86%; MMSE-Sevens item 56%, CI 44%-68%; MMSE-World item 62%, CI 50%-73%). CONCLUSIONS: The MoCA is a suitably accurate, brief test when screening all levels of cognition in PD.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.175 | 0.110 |
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".