Peginterferon Beta-1a Reduces Conversion of MRI Lesions to Black Holes in Patients with Relapsing-Remitting Multiple Sclerosis (P3.091)
Bibliographic record
Abstract
Objective: To investigate the efficacy of peginterferon beta-1a in reducing conversion of new/newly enlarging T2 lesions and new gadolinium-enhancing (Gd+) lesions to black holes in patients with relapsing-remitting multiple sclerosis (RRMS). Background: Subcutaneous peginterferon beta-1a 125 mcg every 2 weeks significantly improved imaging endpoints in patients with RRMS in the Phase 3 ADVANCE study. Methods: Patients were randomized to treatment with peginterferon beta-1a every 2 or 4 weeks or placebo for Year 1; placebo-treated patients then received peginterferon beta-1a in Year 2 (delayed treatment). This post-hoc analysis compared black-hole conversion at the end of Year 2 between patients treated continuously every 2 weeks (n=408) and those receiving delayed treatment (n=393). Results: Patients receiving every-2-week continuous treatment had lower mean numbers of new/newly enlarging T2 (2.6 vs 6.9; 4.1 vs 13.2) and new Gd+ (0.3 vs 1.6; 0.2 vs 1.6) lesions vs delayed treatment (Week 24; Week 48, respectively) compared with baseline. At Week 96, every-2-week continuously-treated patients had significantly fewer black holes converted from new/newly enlarging T2 lesions at Weeks 24 (0.76 vs 1.03 [95[percnt] CI 0.60-0.91], P=0.0037) and 48 (0.44 vs 0.99 [0.34-0.57], P<0.0001), and from new Gd+ lesions at Weeks 24 (0.15 vs 0.32 [0.33-0.67], P<0.0001) and 48 (0.09 vs 0.19[0.29-0.69], P=0.0003), compared with delayed-treatment patients. The proportion of patients who had black holes converted from new/newly enlarging T2 lesions was significantly lower in the continuous every-2-week arm than in the delayed-treatment arm (Week 24: 60.7[percnt] vs 75.1[percnt], P=0.0006; Week 48: 33.9[percnt] vs 63.8[percnt], P<0.0001); there were no significant differences in Gd+ results. Conclusions: Subcutaneous peginterferon beta-1a every 2 weeks significantly reduced conversion of new/newly enlarging T2 lesions and new Gd+ lesions to black holes versus delayed-treatment patients, thereby preventing tissue damage in patients with RRMS and indicating the benefit of early treatment initiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".