Predictive Value of Early MRI Measures for Long-Term Disease Activity in Patients with Relapsing-Remitting Multiple Sclerosis Receiving IFN β-1a SC tiw or IFN β-1a IM qw: Post Hoc Analyses of the EVIDENCE Study (P6.189)
Bibliographic record
Abstract
Objective: Examine early MRI differences between interferon beta-1a (IFN β-1a) treatments and the relationship of baseline lesions to subsequent no evidence of disease activity (NEDA) status. Background: In EVIDENCE, patients with RRMS were randomized to IFN β-1a 44 µg subcutaneously (SC) 3x/week (n=339) or IFN β-1a 30 µg intramuscularly (IM) 1x/week (n=338). Methods: Post hoc analyses assessed whether baseline (Week [W] 0) lesions predicted NEDA up to W48 (no relapses, no disability progression [≥1-point increase in EDSS score sustained for 12 weeks], and no active T2 lesions up to W48 [7 MRI scans]) and W72 (no relapses, no disability progression, and no active T2 lesions up to W72 [8 MRI scans]). Results: IFN β-1a SC was associated with fewer mean Gd+ and active T2 lesions/patient/scan by W8 and W12, respectively, versus IFN β-1a IM (Gd+ 0.79 vs 1.34, p=0.002; T2 0.42 vs 0.55, p=0.008). More IFN β-1a SC patients achieved NEDA up to W48 (33.2[percnt] vs 18.9[percnt], p<0.001) and W72 (26.4[percnt] vs 11.1[percnt], p<0.001) versus IFN β-1a IM. Baseline Gd+ lesions did not differ significantly between groups; presence (vs absence) predicted NEDA status up to W72 for IFN β-1a IM (2.8[percnt] vs 19.4[percnt], p=0.022), but not for IFN β-1a SC (16.9[percnt] vs 36.3[percnt], p=0.187). More patients with and without baseline Gd+ lesions achieved NEDA up to W48 (p≤0.017) and W72 (p≤0.003) with IFN β-1a SC versus IFN β-1a IM. Conclusions: IFN β-1a SC demonstrated early MRI benefits and was associated with more patients (either with or without baseline Gd+ lesions) achieving NEDA, versus IFN β-1a IM. For IFN β-1a IM, baseline Gd+ lesions were associated with reduced likelihood of having NEDA. Study supported by: EMD Serono, Inc., Rockland, MA, USA (a business of Merck KGaA, Darmstadt, Germany); Pfizer Inc, New York, NY, USA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".