Montreal Cognitive Assessment in a Greek sample of patients with multiple sclerosis: A validation study
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is a brief cognitive instrument for the measurement of dementia. The aim of the present study was to measure the sensitivity of this test in a group of Greek speaking participants diagnosed with multiple sclerosis. 40 MS participants complaining for cognitive dysfunction were matched in age and education to 490 healthy participants. The MoCA test and a neuropsychological test battery were administered to both groups. The MoCA test was found to differentiate the MS from the controls (U = 3761.00, p < .001) and it was correlated with all neuropsychological tests (digit span: r = 0.454, p < .0001; phonemic verbal fluency: r = 0.390, p < .0001; semantic verbal fluency: r = 0.319, p < .0001; Color Trails Test 1 (CTT1): r = −.256, p < .0001; Color Trails Test 2 (CTT2): r = −.321, p < .0001). Multiple regression analysis showed that 10.3% of the variation in the MoCA score was accounted for by the Expanded Disability Status Scale (EDSS) total score. Also, the test showed high discriminant validity (optimal screening cut off point 25, sensitivity 0.68, specificity 0.89). MoCA is a sensitive test to differentiate cognitive impairment in Greek speaking MS participants from healthy controls. Further research is needed to use it in larger clinical samples and in different subtypes of the disease.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".