Multiple Sclerosis Relapses Following Cessation of Fingolimod
Bibliographic record
Abstract
BACKGROUND: There is growing interest in the issue of disease reactivation in multiple sclerosis following fingolimod cessation. Relatively little is known about modifiers of the risk of post-cessation relapse, including the delay to commencement of new therapy and prior disease activity. OBJECTIVE: We aimed to determine the rate of relapse following cessation of fingolimod and to identify predictors of relapse following cessation. METHODS: Data were extracted from the MSBase registry in March 2019. Inclusion criteria were (a) clinically definite relapsing multiple sclerosis, (b) treatment with fingolimod for ≥ 12 months, (c) follow-up after cessation for ≥ 12 months, and (d) at least one Expanded Disability Status Scale score recorded in the 12 months before cessation. RESULTS: A total of 685 patients were identified who met criteria. The mean annualised relapse rate was 1.71 (95% CI 1.59, 1.85) in the year prior to fingolimod, 0.50 (95% CI 0.44, 0.55) on fingolimod and 0.43 (95% CI 0.38, 0.49) after fingolimod. Of these, 218 (32%) patients experienced a relapse in the first 12 months. Predictors of a higher relapse rate in the first year were: younger age at fingolimod cessation, higher relapse rate in the year prior to cessation, delaying commencement of new therapy and switching to low-efficacy therapy. CONCLUSIONS: Disease reactivation following fingolimod cessation is more common in younger patients, those with greater disease activity prior to cessation and in those who switch to a low-efficacy therapy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".