Randomized controlled trial of interferon-beta-1a in secondary progressive MS
Bibliographic record
Abstract
OBJECTIVE: To examine MRI changes resulting from treatment of secondary progressive MS (SPMS) with two doses of interferon-beta-1a (Rebif). BACKGROUND: Interferon-beta (IFN-beta) reduces relapses and delays progression in relapsing-remitting MS, but there are conflicting results on its clinical benefit in SPMS. METHODS: In a double-blind, randomized, multicenter, placebo-controlled study (SPECTRIMS), 618 patients received IFN-beta-1a 22 microg, 44 microg, or placebo subcutaneously three times weekly for 3 years. T2 activity and burden of disease (BOD) were measured in 617 patients by using semiannual proton density/T2-weighted (PD/T2) MRI scans. A cohort of 283 patients also had 11 monthly PD/T2 and T1-weighted gadolinium-enhanced (T1-Gd) scans at study start. RESULTS: Treatment reduced median numbers of active lesions per patient per scan (semiannual T2 activity: 0.17, 0.20 and 0.67 for the high dose, low dose, and placebo, p < 0.0001; monthly combined unique activity [T1+T2]: 0.11, 0.22, and 1.00, p < 0.0001) and accumulation of BOD (percent change from baseline to month 36: -1.3, -0.5, and 10.0 for the high dose, low dose, and placebo, respectively; p = 0.0001). MRI benefit was most evident in the subgroup of patients who reported relapses in the 2 years before the study. Neutralizing antibody development was associated with reduction in treatment effect: antibody-positive patients did not show significant differences from placebo at either dose. CONCLUSIONS: Interferon-beta-1a used in SPMS showed significant effects on all MRI measures, particularly in patients with relapses in the 2 years before the study.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".