Clinical features of cognitive dysfunction in patients with relapsing-remitting type of multiple sclerosis
Bibliographic record
Abstract
Background. Cognitive dysfunction in patients with multiple sclerosis is quite common, but attention is not always paid to it, since the decline of cognitive functions is often masked by motor, sensory, and visual disorders. Active patient questioning and neurocognitive screening are needed to identify cognitive impairment in patients with multiple sclerosis, even in the early stages of the disease. The goal of the study is to determine the frequency, severity, and clinical features of cognitive impairment in patients with relapsing-remitting multiple sclerosis, taking into account the duration of the disease and the level of disability of the patients. Materials and Methods. 67 patients with a diagnosis of relapsing-remitting multiple sclerosis were examined. All examined patients underwent a thorough neurological, psychometric, and instrumental examination. Patients were divided into 3 groups depending on the duration of the disease: 1st group up to 5 years (24 patients), 2nd group – from 5 to 10 years (22 patients), 3rd group more than 10 years (21 patients). The Symbol Digit Modalities Test (SDMT) and the Montreal Cognitive Function Assessment Scale (MoCA) were used to assess patients’ neuropsychological status. Results. The conducted correlation analysis showed the presence of a probable inverse relationship between the score on the EDSS scale and the scores on the SDMT and MoSA scales (r = –0.61 (p0.05); r = –0.12 (p>0.05) for scores on SDMT and MoCA scales, respectively). We also obtained a probable directly proportional correlation between the test scores on the MoСA scale and SDMT (in 1st group = 0.63, p<0.05, in 2nd group = 0.89, p<0.05, in the 3rd group r = 0.64, p<0.05) in all studied groups, i.e. for all periods of the disease duration. Conclusions. The obtained data of the correlation analysis indicate a relationship between the severity of cognitive impairment according to the test scores, the degree of disability of the patients, and the duration 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".