Switching to natalizumab or fingolimod in multiple sclerosis: Comparative effectiveness and effect of pre-switch disease activity
Bibliographic record
Abstract
BACKGROUND: Patients with relapsing-remitting multiple sclerosis (RRMS) who experience relapses on a first-line therapy (interferon, glatiramer acetate, dimethyl fumarate, or teriflunomide; collectively, "BRACETD") often switch to another therapy, including natalizumab or fingolimod. Here we compare the effectiveness of switching from a first-line therapy to natalizumab or fingolimod after ≥1 relapse. METHODS: Data collected prospectively in the MSBase Registry, a global, longitudinal, observational registry, were extracted on February 6, 2018. Included patients were adults with RRMS with ≥1 relapse on BRACETD therapy in the year before switching to natalizumab or fingolimod. Included patients received natalizumab or fingolimod for ≥3 months after the switch. RESULTS: Following 1:1 propensity score matching, 1000 natalizumab patients were matched to 1000 fingolimod patients. Mean (standard deviation) follow-up time was 3.02 (2.06) years after switching to natalizumab and 2.58 (1.64) years after switching to fingolimod. Natalizumab recipients had significantly lower annualized relapse rate (relative risk=0.66; 95% confidence interval [CI], 0.59-0.74), lower risk of first relapse (hazard ratio [HR]=0.69; 95% CI, 0.60-0.80), and higher confirmed disability improvement (HR=1.27; 95% CI, 1.03-1.57) than fingolimod recipients. No difference in confirmed disability worsening was observed. CONCLUSIONS: Patients with RRMS switching from BRACETD demonstrated better outcomes with natalizumab than with fingolimod.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".