Multiple Sclerosis Relapses Following Cessation of Fingolimod
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it