Value of the MoCA Test as a Screening Instrument in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Since a large proportion of multiple sclerosis (MS) patients exhibit cognitive deficits, it is important to have reliable and cost-effective screening measures that can be used to follow patients effectively. the objective of this study was to evaluate the clinical value of the Montreal Cognitive Assessment (MoCA) test in detecting cognitive deficits in MS patients. METHODS: Forty-one (70.1% women, mean age 44.51 ±7.43) mildly impaired (EDSS: 2.26 ±1.87) MS patients were recruited for this study. In addition to the MoCA, they were administered the MSNQ-P (patient version) and the MSNQ-I (informant version), the bDI-FS and a comprehensive neuropsychological test battery. RESULTS: there were significant correlations between the MoCA test and the three factors derived from the neuropsychological evaluation (Executive/speed of processing, Learning, Delayed recall). the MoCA test was correlated with the MSNQ-I but only marginally with the MSNQ-P. In addition, there was no significant correlation between the MSNQ-P and the neuropsychological factors, whereas significant correlations were found between two of those factors (Learning and Delayed recall) and the MSNQ-I, suggesting that the informant version is more reliable than the patient version for the presence of cognitive deficits. CONCLUSION: the results obtained in the present study support the value of the MoCA test as a screening tool for the presence of cognitive dysfunction in MS patients, even in patients with mild functional disability (EDSS).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".