099 Safety and effectiveness of dimethyl fumarate in multiple sclerosis patients treated over 5 years
Bibliographic record
Abstract
Introduction In clinical studies, delayed-release dimethyl fumarate (DMF) demonstrated a favorable benefit-risk profile in patients with relapsing-remitting multiple sclerosis (MS). Real-world studies enable char- acterization of risks that may emerge with long-term exposure in clinical practice. ESTEEM ( NCT02047097 ) is an ongoing 5-year study characterizing real-world long-term safety and effectiveness of routinely pre- scribed DMF in MS patients. Methods Patients treated with DMF were recruited from ~380 sites. The primary objective was to determine incidence, type, and pattern of serious adverse events (SAEs), and AEs leading to DMF discontinuation. Results As of April 3, 2019, 5804 patients had ≥1 dose of DMF. SAEs were experienced by 245 (4.8%) patients, with infections (n=64; 1.3%) and nervous system disorders (n=35; <1%) the most common. There were 1676 (33.0%) permanent treatment discontinuations. Annualized relapse rate over the period of up to 5 years was significantly lower than in the year prior to baseline (risk reduction 88.6% [95% confidence interval: 87.7–89.4]; P<0.0001). Conclusions These results reveal low risk of SAE and beneficial therapeutic effects over up to 5 years of real-world DMF use. Updated safety and efficacy results for DMF patients in the United Kingdom will be presented. Support.Biogen. Disclosures: Included on the poster. Ben.newth@biogen.com
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".