Early Onset and Predictive Value of MRI Measures among Patients Receiving Interferon Beta-1a SC tiw for RRMS: Post Hoc Analyses of PRISMS-2 Data (P7.254)
Bibliographic record
Abstract
OBJECTIVE: Examine the temporal relationship between onset of interferon beta-1a (IFN β-1a) subcutaneously (SC) 3x/week (tiw) treatment and MRI measures, and the impact of early treatment effects on long-term clinical outcomes. BACKGROUND: In 2-year data from the placebo-controlled PRISMS-2 study, IFN β-1a 44 or 22 µg SC tiw reduced relapses and T2 lesions and delayed progression in relapsing-remitting multiple sclerosis. DESIGN/METHODS: A subgroup from PRISMS-2 (included in this post hoc analysis; n=205) had monthly proton-density/T2 and T1 gadolinium-enhancing (Gd+) scans before and during the first 9 months of treatment. This analysis investigated monthly percentages of patients free of Gd+ lesions and cumulative mean numbers of active (new or enlarging) T2, Gd+, and combined unique active (CUA; Gd+ and non-enhancing active T2) lesions/patient/scan. The predictive value of presence of Gd+ lesions at 3 months on annualized relapse rate (ARR) was assessed at 1, 2, 3, and 4 years. RESULTS: Significantly larger proportions of patients receiving IFN β-1a 44 µg SC tiw were free of Gd+ lesions from Months 2-9 versus placebo (Month 2: 81.3[percnt] vs 48.4[percnt], p=0.0002; Month 9: 88.2[percnt] vs 52.3[percnt], p=0.0001). IFN β-1a SC tiw was associated with significantly fewer mean active T2 lesions/patient/scan versus placebo beginning Month 3, and with fewer mean CUA and Gd+ lesions beginning Month 2. Presence of Gd+ lesions at Month 3 predicted greater ARR at each cumulative year analyzed (Years 2, 3, and 4; p<0.02), with marginal significance at 1 year. CONCLUSIONS: Post hoc analyses showed early MRI responses with IFN β-1a SC within 3 months, persisting through Month 9. The early MRI response in Gd+ lesions showed a predictive effect on ARR over later years. Study Supported by: EMD Serono, Inc., Rockland, MA, USA (a subsidiary of Merck KGaA, Darmstadt, Germany) and 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".