Peginterferon β-1a every 2 weeks increased achievement of no evidence of disease activity over 4 years in the ADVANCE and ATTAIN studies in patients with relapsing–remitting multiple sclerosis
Bibliographic record
Abstract
Background: No evidence of disease activity (NEDA) is a composite measurement, incorporating clinical and magnetic resonance imaging (MRI) elements of disease activity to sensitively evaluate the therapeutic efficacy of treatments for relapsing–remitting multiple sclerosis (RRMS). Objective: To assess the NEDA status of patients treated with peginterferon β-1a in the ADVANCE and ATTAIN studies and explore its predictive value on longer-term clinical outcomes. Methods: ATTAIN was a 2-year extension of the pivotal 2-year ADVANCE study of peginterferon β-1a for RRMS. Achievement of clinical NEDA, MRI NEDA, or overall NEDA was calculated cumulatively and by year over 4 years. Clinical outcomes during ATTAIN were analyzed based on NEDA status at the end of ADVANCE. Results: Significantly more patients treated with peginterferon β-1a every 2 weeks than every 4 weeks achieved clinical NEDA (60.6% versus 50.6%, p = 0.0063) and MRI NEDA (28.3% versus 15.8%, p = 0.0005) through year 4 and overall NEDA through year 3 (20.9% versus 13.9%, p = 0.0160). Over 4 years, 15.8% of patients in the every 2 weeks group and 10.7% of patients in the every 4 weeks group maintained overall NEDA ( p = 0.0584). Achievement of clinical NEDA, MRI NEDA, or overall NEDA in ADVANCE was predictive of annualized relapse rate in ATTAIN; achievement of clinical NEDA in ADVANCE was also predictive of NEDA achievement and confirmed disability worsening in ATTAIN. Conclusions: Peginterferon β-1a every 2 weeks is associated with higher levels of NEDA compared with placebo in year 1 or peginterferon β-1a every 4 weeks in years 2–4. Overall NEDA within the first 2 years of treatment may be prognostic of long-term clinical outcomes. Clinicaltrials.gov: NCT01332019
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".