Comparative effectiveness of dimethyl fumarate versus non-specific immunosuppressants: Real-world evidence from MSBase
Bibliographic record
Abstract
Background The use of non-specific immunosuppressants (NSIS) to treat multiple sclerosis (MS) remains prevalent in certain geographies despite safety concerns, likely due to resource limitations. Objective To use MSBase registry data to compare real-world outcomes in adults with relapsing-remitting MS (RRMS) treated with dimethyl fumarate (DMF) or NSIS (azathioprine, cyclosporine, cyclophosphamide, methotrexate, mitoxantrone or mycophenolate mofetil) between January 1, 2014 and April 1, 2022. Methods Treatment outcomes were compared using inverse probability of treatment weighting (IPTW) Cox regression. Outcomes were annualized relapse rates (ARRs), time to discontinuation, time to first relapse (TTFR) and time to 24-week confirmed disability progression (CDP) or 24-week confirmed disability improvement (CDI; in patients with baseline Expanded Disability Status Scale [EDSS] score ≥2). Results After IPTW, ARR was similar for DMF (0.13) and NSIS (0.16; p = 0.29). There was no difference in TTFR between cohorts (hazard ratio [HR]: 0.98; p = 0.84). The DMF cohort experienced longer times to discontinuation (HR: 0.75; p = 0.001) and CDP (HR: 0.53; p = 0.001), and shorter time to CDI (HR: 1.99; p < 0.008), versus the NSIS cohort. Conclusion This analysis supports the use of DMF to treat patients with relapsing forms of MS, and may have implications for MS practices in countries where NSIS are commonly used to treat RRMS.
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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".