P.016 Clinical course of relapsing remitting multiple sclerosis post-natalizumab
Bibliographic record
Abstract
Background: Natalizumab is an efficacious disease modifying therapy (DMT) for relapsing remitting multiple sclerosis (RRMS), however, duration of therapy is often limited by risk of progressive multifocal leukoencephalopathy (PML). We describe the clinical course of RRMS patients switched from natalizumab to another DMT in a Canadian MS clinic. Methods: We conducted a retrospective study of prospectively collected data from the Dalhousie Multiple Sclerosis Research Unit (DMSRU). We identified all RRMS patients treated with natalizumab for >3 months who discontinued therapy with serum JC virus antibody positive status and switched to another DMT. Results: There were 84 individuals who switched to another DMT following natalizumab with 57 (68%) switching to fingolimod. Survival without a relapse on fingolimod was 92% (95% confidence interval 80-97%) at 6 months, 90% (77-96%) at 12 months, 85% (71-93%) at 24 months, 74% (56-86%) at 36 months. Survival without disease progression on fingolimod was 90% (95% CI 78-96%) at 6 months, 86% (72-93%) at 12 months, 78% (63-88%) at 24 months, 78% (63-88%) at 36 months. Conclusions: Although alternative DMTs may be used post-natalizumab, fingolimod remains an effective therapy with a high proportion of patients remaining free of relapses or progression at 3 years.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.015 | 0.002 |
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".