Peginterferon beta-1a reduces the evolution of MRI lesions to black holes in patients with RRMS: a post hoc analysis from the ADVANCE study
Bibliographic record
Abstract
The presence of chronic black holes, i.e., chronic lesions that are hypointense on T1-weighted images and are indicative of more severe tissue injury, has been increasingly utilized as a surrogate marker of therapeutic outcome in multiple sclerosis. The ADVANCE study was a 2-year, double-blind, pivotal trial evaluating the safety and efficacy of subcutaneous peginterferon beta-1a 125 mcg in 1512 patients with relapsing-remitting multiple sclerosis (RRMS). This report describes the correlation of clinical outcomes with the evolution of acute lesions into chronic black holes in ADVANCE, and the efficacy of peginterferon beta-1a in reducing this evolution. Treatment with peginterferon beta-1a significantly reduced the mean number of new/enlarging T2-weighted (NET2) lesions (0.76 vs. 1.03 from week 24, p = 0.0037; 0.44 vs. 0.99 from week 48, p < 0.0001) and new gadolinium-enhancing (Gd+) lesions (0.15 vs. 0.32 from week 24, p < 0.0001; 0.09 vs. 0.19 from week 48) that evolved into chronic black holes by 2 years. Patients with NET2 or Gd+ lesions at 24 weeks that evolved into chronic black holes showed significantly worse clinical outcomes, including a greater proportion with 12-week (14.9 vs. 8.4%; p = 0.0167) and 24-week (12.3 vs. 7.0%; p = 0.0333) confirmed disability worsening and higher mean annualized relapse rate (0.62 vs. 0.43; p = 0.0118), compared with patients with lesions that did not evolve into black holes. The correlation was independent of treatment. Reduced risk of evolution of new lesions into chronic black holes with peginterferon beta-1a treatment suggests potential to reduce long-term disability in RRMS by preventing irreversible tissue damage.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".