Imaging Correlates of Plaque Location and Cognitive Dysfunction in Multiple Sclerosis
Bibliographic record
Abstract
Background: Cognitive dysfunction plays a significant role in the disease course of multiple sclerosis (MS), yet the imaging correlates of these cognitive changes are not fully understood. Objectives: This study explores the association between plaque location on magnetic resonance imaging (MRI) and cognitive dysfunction in MS. Methods: This cross-sectional study involved 100 subjects diagnosed with relapsing-remitting MS (RRMS), aged 18 - 55 years, who underwent brain MRI between March 2020 and March 2024 at Besat Clinic Imaging Center, Kerman, Iran. Cognitive function was assessed using the montreal cognitive assessment (MoCA) within one week of MRI. The relationship between plaque location and cognitive impairment was analyzed using SPSS version 25. Results: Of the total participants, 76% were categorized as cognition preserved (CP) and 24% as cognition impaired (CI). No significant differences in average age, disease duration, treatment duration, or comorbidities were found between CP and CI patients. However, CI patients had significantly more demyelinating plaques in the frontal lobe and corpus callosum (P = 0.006 for frontal lobe, P = 0.004 for corpus callosum) compared to CP patients. Conclusions: The distribution of demyelinating plaques in the frontal lobe and corpus callosum may contribute to cognitive dysfunction in MS patients.
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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) | 0.000 | 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".