Discontinuing disease-modifying therapy in MS after a prolonged relapse-free period: a propensity score-matched study
Bibliographic record
Abstract
BACKGROUND: Discontinuation of injectable disease-modifying therapy (DMT) for multiple sclerosis (MS) after a long period of relapse freedom is frequently considered, but data on post-cessation disease course are lacking. OBJECTIVES: (1) To compare time to first relapse and disability progression among 'DMT stoppers' and propensity-score matched 'DMT stayers' in the MSBase Registry; (2) To identify predictors of time to first relapse and disability progression in DMT stoppers. METHODS: Inclusion criteria for DMT stoppers were: age ≥18 years; no relapses for ≥5 years at DMT discontinuation; follow-up for ≥3 years after stopping DMT; not restarting DMT for ≥3 months after discontinuation. DMT stayers were required to have no relapses for ≥5 years at baseline, and were propensity-score matched to stoppers for age, sex, disability (Expanded Disability Status Score), disease duration and time on treatment. Relapse and disability progression events in matched stoppers and stayers were compared using a marginal Cox model. Predictors of first relapse and disability progression among DMT stoppers were investigated using a Cox proportional hazards model. RESULTS: Time to first relapse among 485 DMT stoppers and 854 stayers was similar (adjusted HR, aHR=1.07, 95% CI 0.84 to 1.37; p=0.584), while time to confirmed disability progression was significantly shorter among DMT stoppers than stayers (aHR=1.47, 95% CI 1.18 to 1.84, p=0.001). The difference in hazards of progression was due mainly to patients who had not experienced disability progression in the prebaseline treatment period. CONCLUSIONS: Patients with MS who discontinued injectable DMT after a long period of relapse freedom had a similar relapse rate as propensity score-matched patients who continued on DMT, but higher hazard for disability progression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".