Efficacy of Disease-Modifying Therapies in Relapsing Remitting Multiple Sclerosis: A Systematic Comparison
Bibliographic record
Abstract
The treatment of relapsing-remitting multiple sclerosis (RRMS) has become more effective over the last decade with the advent of the currently available disease-modifying therapies (DMTs). Pivotal clinical studies differ in many characteristics, such that cross-comparisons of relative risk reductions are of limited value and can be misleading. Our objective was to compare the clinical efficacy of currently approved first-line DMTs in patients with RRMS, applying an evidence-based medicine approach. We reviewed all phase III pivotal trials of DMTs. Six clinical trials of Avonex, Betaseron, Copaxone, Rebif and Tysabri in patients with RRMS were identified for analysis. Only randomized, placebo-controlled, double-blind studies were included. The clinical efficacy endpoints compared were: proportion of relapse-free patients at 1 and 2 years; annualized relapse rate at 2 years; proportion of progression-free patients at 2 years, and proportion of patients free of gadolinium-enhancing lesions at 1 year or 9 months. Based on these analyses, Betaseron, Rebif, and Tysabri show comparable effects, whereas for several endpoints Avonex or Copaxone did not significantly differ from placebo. In the absence of head-to-head studies for all products used to treat RRMS, it still may be possible to compare treatment effects by applying evidence-based medicine principles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".