Characteristics of cognitive impairment in multiple sclerosis patients depending on different risk factors
Bibliographic record
Abstract
Cognitive impairment (CI), along with motor deficit, is a crucial component of a disability aggravation in multiple sclerosis (MS) patients. The aim of this study was to asses characteristics of CI in separate cognitive domains depending on socio-demographic (age, sex, level of education), disease parameters (severity, course type and disease duration) and external factors (smoking). The current study enrolled 137 MS patients (102 women and 35 men) aged from 22 to 69 years. All participants were divided into two groups depending on the disease course: group А – patients with relapsing-remitting (RR-MS) type (n=106) and group B – participants with progressive forms of the disease (n=31). The following study discovered that disruption of separate cognitive domains was present even without the apparent CI according to MоCA (Montreal Cognitive Assessment): executive functions impairment (p=0,0013) was found most frequently in case of RR-MS, and memory (p=0,0233) decline in case of progressive forms. In the group A moderate CI were associated with decrease of memory (p<0,0001), attention (p=0,0061), executive functions (p=0,0005), language (p=0,0080) and abstract thinking (p=0,0018); severe CI – with disorders of attention (p=0,0055), language (p<0,0001) and abstract thinking (p=0,0144). As for the group B, moderate CI were associated with decline of abstract thinking (p<0,0001), and severe CI – with impairment of memory and executive functions (p=0,0337). Level of physical disability and smoking impact CI independently of MS course, meanwhile, presence of higher education proves to be beneficial for preserving cognitive functions. In addition, disease duration, number of exacerbations and male gender (concerning attention decline) can affect cognition in relapsing-remitting course of MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".