Cognitive Impairment in Multiple Sclerosis: Clinical, Radiologic and Pathologic Insights
Bibliographic record
Abstract
Cognitive impairment is a common and debilitating feature of multiple sclerosis (MS) that has only recent gained considerable attention. Clinical neuropsychological studies have made apparent the multifaceted nature of cognitive troubles often encountered in MS and continue to broaden our understanding of its complexity. Radiographic studies have started to decipher the neuroanatomic substrate of MS-related cognitive impairment and have shed light onto its pathogenesis. Where radiographic studies have been limited by inadequate resolution or non-specificity, pathological studies have come to the fore. This review aims to provide an overview of the nature of cognitive impairment typically seen in MS and to explore the literature on imaging and pathological studies relevant to its evolution. In particular, the relative contributions of gray (i.e., cerebral cortex, hippocampus, thalamus and basal ganglia) and white matter to MS-related cognitive impairment will be discussed and the importance of interconnectivity between structures highlighted. The pressing need for longitudinal studies combining standardized neuropsychometric, paraclinical and radiographic outcomes obtained during life with post-mortem tissue analysis after death is presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".