Importance of Identifying Cognitive Impairment in Multiple Sclerosis
Bibliographic record
Abstract
This article aims to highlight the impact of cognitive impairment on outcomes and quality of life for people with multiple sclerosis (MS) and to review current evidence for the efficacy of disease-modifying therapies (DMTs) and other interventions. In addition, we provide clinical practice insights regarding screening and management of cognitive impairment in people with MS. Evidence suggests that cognitive deterioration often accompanies magnetic resonance imaging changes. Neocortical volume and deep grey matter atrophy correlate with cognitive impairment. Similarly, cognitive decline is predictive of a higher lesion burden. Cognitive impairment is an important clinical measure of disability and negatively impacts quality of life. Phase 3 studies suggest that DMTs such as natalizumab, ozanimod and fingolimod may provide long-lasting, clinically meaningful effects on cognition in people with MS. Further data are needed to support the use of adjunct cognitive behavioural and exercise interventions for people with MS who have cognitive impairment. More data are needed to define appropriate management strategies for cognitive impairment in people with MS. Baseline and periodic screening for cognitive impairment and inclusion of cognitive impairment as a clinical trial endpoint will help to inform efforts to manage this important aspect of MS.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".