2221 Evaluating no evidence of disease activity (NEDA) with Ozanimod in patients with relapsing multiple sclerosis (RMS): post hoc analysis of phase 3 RADIANCE and DAYBREAK
Bibliographic record
Abstract
Objective To assess NEDA-3 and NEDA-4 in RMS patients treated with ozanimod. Methods Data are from a randomized phase 3 trial (RADIANCE-NCT02047734) of oral ozanimod 0.92 mg/d vs intramuscular interferon β-1a (IFN) 30 µg/wk and an open-label extension trial (DAYBREAK-NCT02576717) of ozanimod 0.92 mg/d. NEDA-3 (no gadolinium-enhancing lesions, new/enlarging T2 lesions, relapses, and Expanded Disability Status Scale score progression) and NEDA-4 (NEDA-3 plus annualized whole brain volume loss ≤0.4%) were calculated from RADIANCE baseline and rebaselined to RADIANCE month 12 to control for high lesion activity and brain volume loss rates immediately after treatment initiation (observed cases). Results NEDA-3 rates at RADIANCE month 12 and 24 and DAYBREAK month 12, 24, and 36 were 31.2%, 24.6%*, 16.2%*, 13.4%*, and 10.7% with continuous ozanimod and 26.9%, 17.0%, 9.8%, 8.6%, and 7.4% for those on/transitioned from IFN (IFN→ozanimod), respectively. NEDA-4 rates were 21.5%, 14.0%*, 10.0%, 10.4%, and 10.3% for continuous ozanimod and 16.3%, 7.8%, 5.9%, 6.2%, and 6.3% for IFN→ozanimod. After rebaselining to month 12, NEDA-3 rates at RADIANCE month 24 and DAYBREAK month 12, 24, and 36 were 52.6%*, 33.1%*, 26.3%*, and 21.3% with continuous ozanimod and 33.4%, 20.5%,17.4%, and 14.8% for IFN→ozanimod. Rebaselined rates of NEDA-4 were 33.5%*, 20.0%*, 16.7%, and 14.1% for continuous ozanimod and 19.7%, 11.7%, 11.2%, and 11.0% for IFN→ozanimod. Conclusions More patients achieved NEDA-3 and NEDA-4 at month 24 with ozanimod vs IFN. Rebaselining to month 12 resulted in more patients on continuous ozanimod vs IFN→ozanimod achieving NEDA-3 and NEDA-4 in DAYBREAK. *P<0.05 vs IFN.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".