The Relationship between Psychosocial Factors and Cognition in Multiple Sclerosis
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) is a common disorder in some regions of the world, with over 2.3 million people diagnosed worldwide. Cognitive impairment is one of the earliest symptoms to present in the course of the disease and can cause significant morbidity. We proposed a study to explore the psychosocial predictors of cognitive impairment in MS patients in Saudi Arabia, a previously unexplored patient population. METHODS: Demographic data, depression scale (PHQ9), symptom burden (PHQ15), anxiety (GAD7), disease duration, and Montreal Cognitive Assessment (MOCA) scores were collected from 195 patients in a neurology clinic in Ryiadh, Saudi Arabia. Univariate and multiple regression analyses were conducted to identify variables that are significantly associated with cognitive impairment. RESULTS: < 0.05, were education level, disease duration, and family history. DISCUSSION: Both education level and disease duration were variables identified in previous studies. We showed family history to be a significant variable, and no association was found with depression or anxiety, which is unique to our study population. CONCLUSIONS: We identified several psychosocial predictors that are associated with cognition in our patient population. It was also noted that a difference exists between patient populations, highlighting the need for further studies in specific geographical regions.
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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 | 0.001 | 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) | 0.002 | 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".