Screening of Cognitive Impairment in Patients with Multiple Sclerosis: A Cross-Sectional Study in Georgia
Bibliographic record
Abstract
Cognitive impairment (CI) is a common symptom of multiple sclerosis (MS), with a significant negative impact on the occupational and social functioning of patients. This study aimed to estimate the prevalence and characteristics of CI among MS patients in Georgia. Sixty-eight patients with MS attending a neurology outpatient clinic in Tbilisi, Georgia, were enrolled in the study. Cognitive status was evaluated using two screening tools: the Brief International Cognitive Assessment for MS and the Montreal Cognitive Assessment. The overall prevalence of CI in our MS patients was 47%. We found negative associations between cognitive test results and patients' age, disability status, and depression. Lower education, higher scores on the Expanded Disability Status Scale, and the progressive course of MS were the main predictors of CI in the logistic regression analysis. This is the first study in Georgia to evaluate CI in patients with MS. The prevalence of CI in our study was comparable with those reported in other countries; however, we found greater impairment of the executive system compared to other cognitive domains. In our study, patients who were on continuous DMT showed significantly better performance on the cognitive tests used, indicating possible favorable effect of immunomodulatory drugs on cognition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".