Extended window of relapse recovery in RRMS: an analysis of the DECIDE dataset
Bibliographic record
Abstract
BACKGROUND: The main goal of treatment in relapsing-remitting multiple sclerosis (RRMS) is to reduce the occurrence of relapses. However, little is known about the natural history of relapse recovery. METHODS: We accessed data from DECIDE (n=1841), a phase 3 trial. We investigated factors associated with time to relapse recovery (defined as a return of the Expanded Disability Status Scale (EDSS) score to the pre-relapse level or lower), relapse severity (0.5, 1.0, or >1.0 EDSS score change) and the new concept of 'acute clinical events with stable MRI' (ACES). Variables used were age, sex, disease duration, treatment arm, pre-relapse EDSS, corticosteroid use, number of relapses prior to study enrolment, MRI activity, relapse severity and affected functional system (FS). RESULTS: We included 430 first relapses, of which 405 (94.2%) recovered during follow-up, 400 (93%) by 1 year (median time to recovery of 71 days, 95% CI 66 to 75 days). More severe relapses and relapses involving the bowel and bladder FS took a longer time to recover. Corticosteroids hastened the recovery of relapses but did not influence eventual relapse recovery. ACES occurred in 38% of relapses and was more frequent in older people and participants treated with daclizumab. CONCLUSIONS: Most relapses (94.2%) recover, but the process of recovery can take up to 1 year and depends mostly on relapse severity. Our findings challenge the concept of 3-month and 6-month confirmed disability progression as reliable markers of permanent disability in RRMS trials. ACES occurs frequently and is associated with age.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".