Are cognitive interventions for Multiple Sclerosis effective and feasible?
Bibliographic record
Abstract
PURPOSE: Fifty percent of patients with Multiple Sclerosis (MS) are estimated to have cognitive impairments leading to considerable decline in productivity and quality of life. Cognitive intervention has been considered to complement pharmacological treatments. However, a lack of agreement concerning the efficacy of cognitive interventions in MS still exists. A systematic review and meta-analysis was conducted to assess the effects of cognitive interventions in MS. METHODS: To overcome limitations of previous meta-analyses, several databases were searched only for Randomized Clinical Trials (RCTs) with low risk of bias. RESULTS: Five studies (total of 139 participants) met our eligibility criteria. Although good completion and adherence rates were evident, we found no evidence of intervention effects on cognition or mood in post-intervention or follow-up assessments. CONCLUSIONS: This is the first meta-analysis assessing the effects of cognitive intervention in MS including only RCTs with comparable conditions. Research regarding efficacy, cost-effectiveness and feasibility is still in its infancy. Caution is advised when interpreting these results due to the small number of RCTs meeting the inclusion criteria. Considering the costs of disease, good completion and adherence rates of this approach, further research is warranted. Recommendations concerning improved research practices in the field are presented as well.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".