Comparison of Montreal Cognitive Assessment test and Mini Mental State Examination in detecting cognitive impairment in relapsing-remitting multiple sclerosis patients
Bibliographic record
Abstract
Background and purpose: Cognitive impairment (CI) is one of the causes of disabilities in multiple sclerosis patients (MS). Therefore, early detection and evaluation of cognitive performance is very important in patients with MS. The aim of the present study is to compare Montreal Cognitive Assessment (MoCA) test and Mini Mental Status Exam (MMSE) in Relapsing Remitting (RR) MS patients. Methods: Fifty RRMS patients who met inclusion and exclusion criteria were recruited in this study. MMSE and MoCA were administrated to all subjects. Also demographic data, disease duration and EDSS were recorded. The results of both tests were compared. Results: The mean score of MoCA and MMSE was 22.86±3.85 and 27.64±2, with a significant difference (p<0.0005). With using MoCA 60% of subject had CI, whereas with MMSE only 34% were impaired (p<0.0005). There was an inverse significant association between education and CI detected by both MMSE and MoCA (for MMSE r=0.535 and p<0.0005, for MoCA r=0.544 and p<0.0005). A significant association was also found between disease duration and CI on both tests (for MMSE r=0.394 and p<0.0005, for MoCA r=0.538 and p<0.0005). Conclusion: This study suggests that the MoCA has superiority to the MMSE for evaluating cognitive function in RRMS 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".