Effect of fingolimod on MRI outcomes in patients with paediatric-onset multiple sclerosis: results from the phase 3 PARADIG<i>MS</i>study
Bibliographic record
Abstract
OBJECTIVE: demonstrated superior efficacy and comparable safety of fingolimod versus interferon β-1a (IFN β-1a) in paediatric-onset multiple sclerosis (PoMS). This study aimed to report all predefined MRI outcomes from this study. METHODS: Patients with multiple sclerosis (MS) (aged 10-<18 years) were randomised to once-daily oral fingolimod (n=107) or once-weekly intramuscular IFN β-1a (n=108) in this flexible duration study. MRI was performed at baseline and every 6 months for up to 2 years or end of the study (EOS) in case of early treatment discontinuation/completion. Key MRI endpoints included the annualised rate of formation of new/newly enlarging T2 lesions, gadolinium-enhancing (Gd+) T1 lesions, new T1 hypointense lesions and combined unique active (CUA) lesions (6 months onward), changes in T2 and Gd+ T1 lesion volumes and annualised rate of brain atrophy (ARBA). RESULTS: Of the randomised patients, 107 each were treated with fingolimod and IFN β-1a for up to 2 years. Fingolimod reduced the annualised rate of formation of new/newly enlarging T2 lesions (52.6%, p<0.001), number of Gd+ T1 lesions per scan (66.0%, p<0.001), annualised rate of new T1 hypointense lesions (62.8%, p<0.001) and CUA lesions per scan (60.7%, p<0.001) versus IFN β-1a at EOS. The percent increases from baseline in T2 (18.4% vs 32.4%, p<0.001) and Gd+ T1 (-72.3% vs 4.9%, p=0.001) lesion volumes and ARBA (-0.48% vs -0.80%, p=0.014) were lower with fingolimod versus IFN β-1a, the latter partially due to accelerated atrophy in the IFN β-1a group. CONCLUSION: Fingolimod significantly reduced MRI activity and ARBA for up to 2 years versus IFN β-1a in PoMS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".