043 Efficacy of siponimod in secondary progressive multiple sclerosis with active disease: EXPAND study subgroup analysis
Bibliographic record
Abstract
Background Siponimod, a selective sphingosine 1-phosphate receptor modulator, demonstrated clini- cally relevant effects in a typical secondary progressive multiple sclerosis (SPMS) population in the Phase 3 EXPAND study, with 21% and 26% reductions in 3- and 6-month confirmed disability progression (CDP) versus placebo. Methods Post hoc subgroup analysis was performed in patients with active disease (defined as the presence of relapses in 2 years before screening and/or ≥1 T1 gadolinium-enhancing [Gd ] lesion at baseline) to assess the efficacy of siponimod 2mg versus placebo in this population.Results This analysis included 779 SPMS patients with active disease (siponimod [n=516], placebo [n=263]). The proportion of patients with relapse in the 2 years prior to study was 76% and with Gd lesions at baseline 45%. Siponimod significantly reduced 3-month CDP risk by 31% (HR [95% CI]: 0.69 [0.53, 0.91]; p=0.0094) and 6-month CDP risk by 37% (HR [95% CI]: 0.63 [0.47, 0.86], p=0.0040) versus placebo. Reductions in risk of 6 month SDMT worsening, ARR, and MRI endpoints were also seen. Conclusions In this subgroup of active SPMS patients from EXPAND, benefits on disability progression were more pronounced with clinically relevant effects across disability progression, cognitive processing speed, and MRI inflammatory disease activity. g.giovannoni@qmul.ac.uk
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".