Phase IV study of retention on fingolimod <i>versus</i> injectable multiple sclerosis therapies: a randomized clinical trial
Bibliographic record
Abstract
Objective: In relapsing–remitting multiple sclerosis (RRMS), suboptimal adherence to injectable disease-modifying therapies (iDMTs; interferon β-1a/b, glatiramer acetate) is common, reducing their effectiveness. Patient retention on oral fingolimod and iDMTs was evaluated in PREFER MS, a randomized, parallel-group, active-controlled, open-label, 48-week study. Methods: Patients were included if they had RRMS, were aged 18–65 years and had Expanded Disability Status Scale score up to 6, enrolled at 117 US study sites, were treatment naïve or had received only one iDMT class. Patients were randomized 1:1 (fingolimod 0.5 mg/day; preselected iDMT) by interactive voice-and-web-response system without blinding, followed up quarterly, and allowed one study-approved treatment switch after 12 weeks, or earlier for efficacy or safety reasons. The primary outcome was patient retention on randomized treatment over 48 weeks. Secondary endpoints included patient-reported outcomes, brain volume loss (BVL), and cognitive function. Results: Analysis of 433/436 patients receiving fingolimod and 428/439 receiving iDMTs showed that patient retention rate was significantly higher with fingolimod than with iDMTs [352 (81.3%) versus 125 (29.2%); 95% confidence interval 46.4–57.8%; p < 0.0001]. The most common treatment switch was from iDMT to fingolimod for injection-related reasons. Patient satisfaction was greater and BVL less with fingolimod than with iDMTs, with no difference in cognitive function. Adverse events were consistent with established tolerability profiles for each treatment. Conclusions: In RRMS, fingolimod was associated with better treatment retention, patient satisfaction and BVL outcomes than iDMTs. Patients may persist with iDMTs, but many may switch treatment if permitted. Treatment satisfaction fosters adherence, a prerequisite for optimal outcomes.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".