The neuropsychiatry of multiple sclerosis: a review of recent developments
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this review is to summarize the current literature on the neuropsychiatry of multiple sclerosis (MS). RECENT FINDINGS: Data from community samples have supported earlier findings from tertiary referral centres of high rates of depression in MS patients. Neuroimaging offers important clues as to the pathogenesis of depression, but psychosocial factors cannot be ignored and emerge as equally important predictors. Cognitive-behavioural therapy is an effective treatment, rivalling standard dosing of sertraline in patients with depression. An allied disorder--pseudobulbar affect--occurs in up to 10% of MS patients and responds well to a combination of dextromethorphan and quinidine. Cognitive dysfunction affects approximately 40% of MS patients. Markers of cerebral atrophy have emerged as more important correlates of impaired cognition than lesion volume. Moreover, functional MRI studies have demonstrated the brain's ability to compensate, in part, for damage. Should the disease burden be too severe, however, compensatory mechanisms fail and cognitive deficits increase accordingly. SUMMARY: Neuropsychiatric abnormalities are common in MS patients. No aspect of mentation is spared. Advances in neuroimaging are increasing our understanding of the pathogenesis of these disorders. Translating these findings into improved methods of treatment for patients presents researchers with pressing challenges.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".