Siponimod: Disentangling disability and relapses in secondary progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: In multiple sclerosis, impact of treatment on disability progression can be confounded if treatment also reduces relapses. OBJECTIVE: To distinguish siponimod's direct effects on disability progression from those on relapses in the EXPAND phase 3 trial. METHODS: Three estimands, one based on principal stratum and two on hypothetical scenarios (no relapses, or equal relapses in both treatment arms), were defined to determine the extent to which siponimod's effects on 3- and 6-month confirmed disability progression were independent of on-study relapses. RESULTS: Principal stratum analysis estimated that siponimod reduced the risk of 3- and 6-month confirmed disability progression by 14%-20% and 29%-33%, respectively, compared with placebo in non-relapsing patients. In the hypothetical scenarios, risk reductions independent of relapses were 14%-18% and 23% for 3- and 6-month confirmed disability progression, respectively. CONCLUSION: By controlling the confounding impact of on-study relapses on confirmed disability progression, these statistical approaches provide a methodological framework to assess treatment effects on disability progression in relapsing and non-relapsing patients. The analyses support that siponimod may be useful for treating secondary progressive multiple sclerosis in patients with or without relapses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".