Managing reactivation of multiple sclerosis during treatment with natalizumab
Bibliographic record
Abstract
BACKGROUND: Following natalizumab failure, it is unknown whether switching to alternative high-efficacy therapies offers superior effectiveness over continuing natalizumab. OBJECTIVE: To compare different treatment strategies following natalizumab failure. METHODS: Patients suffering a relapse during natalizumab treatment with adequate follow-up were identified from the MSBase registry. Following natalizumab failure, natalizumab continuation was compared to switching to anti-CD20 therapies/alemtuzumab/lower-efficacy therapies and treatment discontinuation. The primary outcome was the risk of further relapses. Secondary outcomes included risk of subsequent magnetic resonance imaging (MRI) activity, confirmed disability worsening and disease-activity-free survival. Multivariable proportional hazards models compared outcomes during time-varying therapy exposures. Four sensitivity analyses were conducted with varied inclusion criteria and treatment failure definitions. RESULTS: Of 1553 patients experiencing a relapse during natalizumab treatment, 1037 met the inclusion criteria. Following natalizumab failure, switch to anti-CD20 therapy was associated with a lower relapse risk (heart rate (HR) = 0.48, 95% confidence interval (CI) = 0.27-0.84) compared to continuing natalizumab; no differences were observed in MRI or disability outcomes. Treatment de-escalation or cessation was associated with increased relapse risk (HR = 1.46, 95% CI = 1.15-1.85; HR = 2.08, 95% CI = 1.22-3.55, respectively). We did not find evidence of a difference for switching to alemtuzumab. Sensitivity analyses replicated primary findings. CONCLUSION: This exploratory study indicates that switching to anti-CD20 therapies following natalizumab failure is associated with a >50% reduction in relapse risk. No differences were seen in secondary outcomes, despite consistent trends. Clinicians may consider anti-CD20 therapies following natalizumab failure, noting further research is needed to confirm differences in MRI and disability outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".