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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".