115 Impact of siponimod on myelination across SPMS subgroups: post-hoc analysis from EXPAND MRI substudy
Bibliographic record
Abstract
Background Changes in magnetization transfer ratio (MTR) are a marker of changes in myelin density and brain tissue integrity. Siponimod improved lesional MTR recovery in the overall EXPAND secondary progressive multiple sclerosis (SPMS) population. Objectives Investigate the effect of siponimod on MTR changes in SPMS subgroups. Methods This prospective sub-study assessed the effect of siponimod versus placebo on median nor- malized MTR (nMTR) in normal appearing brain tissue (NABT), cortical Grey Matter (cGM) and normal appearing white matter (NAWM). Subgroups were defined by: disease history, severity and duration, EDSS score, Symbol Digit Modalities Test score, and inflammatory disease activity. Results There was an attenuation in median nMTR decrease versus placebo across all subgroups (all p<0.05 except EDSS≥6 subgroup, p=0.064). In the active SPMS subgroup, siponimod attenuated median nMTR decrease across NABT, cGM and NAWM by 91–109% (p<0.01 all); and in the non-active SPMS subgroup by 170– 198% (p=0.0151 for NAWM, p>0.05 for NABT, cGM). Conclusions Over 24 months, siponimod attenuated the decrease in median nMTR in brain tissues across patient subgroups characterized by disease activity and severity, with most pronounced effects in NAWM. This supports preclinical studies, showing beneficial CNS effects on myelination. teresa.sawtell@novartis.com
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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) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".