Exploring Social Cognition Sub‐Domains and Predictors in Multiple Sclerosis: A Cross‐Sectional Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Social cognition (SC) is increasingly recognized as a key cognitive domain affected in multiple sclerosis (MS), yet its sub-domains and clinical correlates remain underexplored. This study aimed to assess different SC sub-domains and identify their cognitive, emotional, and demographic predictors in people with MS (pwMS). METHODS: This cross-sectional study included 93 pwMS and 34 HCs. Assessments included the Reading the Mind in the Eyes Test (RMET) for emotion recognition, the Trail Making Test (TMT) for executive function, the Tromso Social Intelligence Scale (TSIS) for nonverbal understanding, the Implied Meaning Test (IMT) for implicit understanding, the Social-Emotional Competence Scale for adaptability, the Barratt Impulsiveness Scale for impulsivity, the Stroop Test for inhibition, the Beck Depression Inventory (BDI) for depression, the Montreal Cognitive Assessment (MoCA) for cognition, and the Short Form-12 (SF-12) for quality of life (QoL). Multiple regression analyses were conducted to identify independent predictors of SC performance. RESULTS: PwMS, particularly those with progressive MS, exhibited significantly lower SC performance across all sub-domains compared to HCs. Regression analyses revealed that lower MoCA scores, higher BDI scores, and lower educational attainment were significant predictors of impaired SC, while disease duration and gender were not. Notably, SC deficits were also observed in cognitively preserved individuals, suggesting the relative independence of SC impairments. CONCLUSION: SC impairment is a distinct and clinically relevant feature of MS, associated with both cognitive and emotional factors. Routine SC screening may enhance patient care by informing personalized interventions. Future research should include larger cohorts, longitudinal designs, and practical SC assessment tools for clinical use.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".