Impact of prior treatment status and reasons for discontinuation on the efficacy and safety of fingolimod: Subgroup analyses of the Fingolimod Research Evaluating Effects of Daily Oral Therapy in Multiple Sclerosis (FREEDOMS) study
Bibliographic record
Abstract
BACKGROUND: Fingolimod is a once-daily, oral sphingosine 1-phosphate receptor modulator approved for the treatment of relapsing multiple sclerosis. OBJECTIVE: This post-hoc analysis of phase 3 FREEDOMS data assessed whether the effects of fingolimod are consistent among subgroups of patients defined by prior treatment history. METHODS: Annualized relapse rate and safety profile of treatment with fingolimod 0.5mg, 1.25mg, or placebo once-daily for 24 months were analyzed in 1272 relapsing multiple sclerosis patients, by subgroups based on disease-modifying therapy history (treatment-naive; prior interferon-β or glatiramer acetate), reason for discontinuation of prior disease-modifying therapy (unsatisfactory therapeutic response or adverse events), and prior disease-modifying therapy duration. RESULTS: Both fingolimod doses significantly reduced annualized relapse rate in patients that received prior interferon-β or glatiramer acetate, discontinued prior disease-modifying therapy owing to unsatisfactory therapeutic effect, were treatment-naive, or had prior disease-modifying therapy duration of >1-3 years (P≤0.0301 for all comparisons vs placebo). Fingolimod 1.25mg resulted in greater reductions in annualized relapse rate in patients that discontinued prior disease-modifying therapy for adverse events or had prior disease-modifying therapy duration of ≤1 year or >3 years (P≤0.0194 vs placebo). CONCLUSIONS: Fingolimod demonstrated similar efficacy in relapsing multiple sclerosis patients regardless of prior treatment history. Clinicaltrials.gov identifier: NCT00289978.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".