Switch to natalizumab versus fingolimod in active relapsing–remitting multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: In patients suffering multiple sclerosis activity despite treatment with interferon β or glatiramer acetate, clinicians often switch therapy to either natalizumab or fingolimod. However, no studies have directly compared the outcomes of switching to either of these agents. METHODS: Using MSBase, a large international, observational, prospectively acquired cohort study, we identified patients with relapsing-remitting multiple sclerosis experiencing relapses or disability progression within the 6 months immediately preceding switch to either natalizumab or fingolimod. Quasi-randomization with propensity score-based matching was used to select subpopulations with comparable baseline characteristics. Relapse and disability outcomes were compared in paired, pairwise-censored analyses. RESULTS: Of the 792 included patients, 578 patients were matched (natalizumab, n = 407; fingolimod, n = 171). Mean on-study follow-up was 12 months. The annualized relapse rates decreased from 1.5 to 0.2 on natalizumab and from 1.3 to 0.4 on fingolimod, with 50% relative postswitch difference in relapse hazard (p = 0.002). A 2.8 times higher rate of sustained disability regression was observed after the switch to natalizumab in comparison to fingolimod (p < 0.001). No difference in the rate of sustained disability progression events was observed between the groups. The change in overall disability burden (quantified as area under the disability-time curve) differed between natalizumab and fingolimod (-0.12 vs 0.04 per year, respectively, p < 0.001). INTERPRETATION: This study suggests that in active multiple sclerosis during treatment with injectable disease-modifying therapies, switching to natalizumab is more effective than switching to fingolimod in reducing relapse rate and short-term disability burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".