Clinical and MRI efficacy of sc IFN β-1a tiw in patients with relapsing MS appearing to transition to secondary progressive MS: post hoc analyses of PRISMS and SPECTRIMS
Bibliographic record
Abstract
This study evaluated efficacy of subcutaneous (sc) interferon beta-1a (IFN β-1a) 44 µg 3 × weekly (tiw) in patients appearing to transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS). The PRISMS study included 560 patients with RRMS (EDSS 0-5.0; ≥ 2 relapses in previous 2 years), and the SPECTRIMS study included 618 patients with SPMS (EDSS 3.0-6.5 and ≥ 1-point increase in previous 2 years [≥ 0.5 point if 6.0-6.5]) randomly assigned to sc IFN β-1a 44 or 22 µg or placebo for 2-3 years, respectively. These post hoc analyses examined five subgroups of MS patients with EDSS 4.0-6.0: PRISMS (n = 59), PRISMS/SPECTRIMS (n = 335), PRISMS/SPECTRIMS with baseline disease activity (n = 195; patients with either ≥ 1 relapse within 2 years before baseline or ≥ 1 gadolinium-enhancing lesion at baseline), PRISMS/SPECTRIMS without baseline disease activity (n = 140), and PRISMS/SPECTRIMS with disease activity during the study (n = 202). In the PRISMS and PRISMS/SPECTRIMS subgroups, sc IFN β-1a delayed disability progression, although no significant effect was observed in PRISMS/SPECTRIMS subgroups with activity at baseline or activity during the study (regardless of baseline activity). In the PRISMS/SPECTRIMS subgroup, over year 1 (0-1) and 2 (0-2), sc IFN β-1a 44 µg tiw significantly reduced annualized relapse rate (p ≤ 0.001), and relapse risk (p < 0.05) versus placebo. Similar results were seen for the PRISMS/SPECTRIMS with baseline disease activity subgroup. Subcutaneous IFN β-1a reduced T2 burden of disease and active T2 lesions in the PRISMS/SPECTRIMS subgroups overall, with and without baseline activity. In conclusion, these post hoc analyses indicate that effects of sc IFN β-1a 44 µg tiw on clinical/MRI endpoints are preserved in a patient subgroup appearing to transition between RRMS and SPMS.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".