Relapse recovery in relapsing–remitting multiple sclerosis: An analysis of the CombiRx dataset
Bibliographic record
Abstract
BACKGROUND: Clinical relapses are the defining feature of relapsing forms of multiple sclerosis (MS), but relatively little is known about the time course of relapse recovery. OBJECTIVE: The aim of this study was to investigate the time course of and patient factors associated with the speed and success of relapse recovery in people with relapsing-remitting MS (RRMS). METHODS: Using data from CombiRx, a large RRMS trial (clinicaltrials.gov identifier NCT00211887), we measured the time to recovery from the first on-trial relapse. We used Kaplan-Meier survival analyses and Cox regression models to investigate the association of patient factors with the time to unconfirmed and confirmed relapse recovery. RESULTS: CombiRx included 1008 participants. We investigated 240 relapses. Median time to relapse recovery was 111 days. Most recovery events took place within 1 year of relapse onset: 202 of 240 (84%) individuals recovered during follow-up, 161 of 202 (80%) by 180 days, and 189 of 202 (94%) by 365 days. Relapse severity was the only factor associated with relapse recovery. CONCLUSION: Recovery from relapses takes place up to approximately 1 year after the event. Relapse severity, but no other patient factors, was associated with the speed of relapse recovery. Our findings inform clinical practice and trial design in RRMS.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.003 | 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".