Effect of interferon beta-1a subcutaneously three times weekly on clinical and radiological measures and no evidence of disease activity status in patients with relapsing–remitting multiple sclerosis at year 1
Bibliographic record
Abstract
BACKGROUND: In the PRISMS study, interferon beta-1a subcutaneously (IFN β-1a SC) reduced clinical and radiological disease burden at 2 years in patients with relapsing-remitting multiple sclerosis. The study aimed to characterize efficacy of IFN β-1a SC 44 μg and 22 μg three times weekly (tiw) at Year 1. METHODS: Exploratory endpoints included annualized relapse rate (ARR), 3-month confirmed disability progression (1-point Expanded Disability Status Scale increase if baseline was < 6.0 [0.5-point if baseline was ≥6.0]), active T2 lesions, and no evidence of disease activity (NEDA; defined as no relapses [subanalyzed by relapse severity], 3-month confirmed progression, or active T2 lesions). Effect of IFN β-1a SC in prespecified patient subgroups was also assessed. RESULTS: Patients were randomized to IFN β-1a 22 μg (n = 189), 44 μg (n = 184), or placebo (n = 187). At 1 year, IFN β-1a SC tiw reduced ARR (p < 0.001), risk of disability progression (p ≤ 0.029), and mean number of active T2 lesions per patients per scan (p < 0.001) versus placebo. Clinical and radiological benefits were seen as early as Month 2 and 3. Outcomes in subgroups were consistent with those in the overall population. More patients treated with IFN β-1a SC tiw achieved NEDA status, versus placebo, regardless of relapse severity (p ≤ 0.006). CONCLUSION: Clinical, radiological, and NEDA outcomes at Year 1 were consistent with Year 2 results. Treatment efficacy was consistent in pre-specified patient subgroups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".