086 The rapid efficacy of natalizumab vs fingolimod in patients with active relapsing-remitting multiple sclerosis (RRMS): results from reveal, a randomised, head-to-head study
Bibliographic record
Abstract
Introduction REVEAL was designed as a 1 year, multicentre, randomised, rater- and sponsor-blinded, prospective study comparing natalizumab and fingolimod in patients with active RRMS. Although the study closed early (for non-safety/non-efficacy reasons), data permitted comparison of effects occurring shortly after treatment initiation. This analysis compares onset of efficacy with natalizumab and fingolimod in REVEAL. Methods Patients were randomised to open-label intravenous natalizumab 300 mg every 4 weeks (n=54) or oral fingolimod 0.5 mg once daily (n=54). Magnetic resonance imaging was scheduled every 4 weeks for the first 24 weeks and at weeks 36 and 52. Analyses included Kaplan-Meier and Cox regression, negative binomial regression (annualised relapse rate [ARR] and number of T1 gadolinium-enhancing [Gd+] lesions) and a negative binomial generalised estimating equation (cumulative Gd +lesions over time). Results As expected for a randomised study, patient characteristics and follow-up time (median 39 weeks) were generally similar between groups. Natalizumab patients were less likely than fingolimod patients to develop new Gd +lesions (for ≥1 lesion, cumulative probability 40.68% vs 57.99%; hazard ratio [HR]=1.678 [95% CI: 0.865 to 3.255]; p=0.1258; for ≥2 lesions, cumulative probability 11.54% vs 48.48%; HR=4.053 [95% CI: 1.474 to 11.144]; p=0.007). Natalizumab patients consistently had 63%–72% fewer Gd +lesions than fingolimod patients, with between-group differences apparent within 4 weeks and reaching significance by 12 weeks (p=0.030). ARR was 83% lower with natalizumab than with fingolimod (0.05 vs 0.29; p=0.023), and cumulative probability of relapse was 1.85% with natalizumab vs 22.28% with fingolimod (HR=12.184 [95% CI: 1.552 to 95.634]; p=0.017). Adverse events were consistent with known safety profiles. Conclusion These results suggest that natalizumab reduces disease activity more rapidly and to a greater extent than fingolimod in patients with active RRMS. Given the early study closure, available data did not permit primary endpoint evaluation, and interpretation of these results requires caution. Study Support Biogen.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".