Systematic review and network meta‐analysis comparing ofatumumab with other disease‐modifying therapies available in Japan for the treatment of patients with relapsing multiple sclerosis
Bibliographic record
Abstract
Abstract Objective No head‐to‐head clinical trials have compared ofatumumab with other disease‐modifying therapies (DMTs) available in Japan for patients with relapsing multiple sclerosis (RMS). In this study, a network meta‐analysis (NMA) was conducted to compare the efficacy of ofatumumab to other DMTs currently available in Japan for the treatment of patients with RMS. Methods Systematic searches were conducted in biomedical databases from inception to June 2020 to identify randomized controlled trials. Only English‐ and Japanese‐language publications describing studies conducted in Japan were included. Trials with sufficiently similar study and patient characteristics were included in a Bayesian NMA. A sensitivity analysis was conducted to explore the impact of potential sources of uncertainty. Results Four trials, each comparing a DMT with placebo in a ≥50% Japanese population, were sufficiently similar that comparative efficacy could be assessed for annualized relapse rate (ARR). Ofatumumab numerically reduced ARR compared with fingolimod (rate ratio [RR]: 0.84, 95% credible interval [CrI]: 0.20–3.39), dimethyl fumarate (RR: 0.61, 95% CrI: 0.16–2.30), and placebo (RR: 0.41, 95% CrI: 0.12–1.39), but not natalizumab (RR: 1.33, 95% CrI: 0.33–5.45). In a subgroup analysis of Japanese patients only, ofatumumab reduced relapses compared with all other treatments including natalizumab. These results were limited by the lack of studies reporting direct comparisons between included treatments and by heterogenous reporting of outcome data. Conclusion These findings, although limited by the paucity of evidence for Japanese patients, suggest that monoclonal antibody therapies (ie, natalizumab and ofatumumab) may provide improved efficacy compared with other DMTs available in Japan for patients with RMS.
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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 | 0.033 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".