Combination therapies for multiple sclerosis: scientific rationale, clinical trials, and clinical practice
Bibliographic record
Abstract
PURPOSE OF REVIEW: To outline the scientific rationale for combination therapy in multiple sclerosis and to discuss the evidence for combination treatment strategies from animal models and clinical trials of multiple sclerosis. RECENT FINDINGS: Experiments conducted in experimental autoimmune encephalomyelitis have recently shown beneficial effects of numerous combination therapies. The combination of approved and experimental drugs and two or more experimental agents may positively impact clinical disease activity, inflammation within the central nervous system, and neurorepair. Clinical trials are currently underway to establish the therapeutic efficacy and safety of various combination therapies for multiple sclerosis patients. SUMMARY: More effective therapies are needed to treat multiple sclerosis. There are good scientific rationales for the use of combination therapy in multiple sclerosis, and the pharmacologic principles for evaluating and understanding their actions are available. The evaluation of specific combination therapies in the controlled setting of clinical trials should be a priority in clinical multiple sclerosis research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".