Trial of Fingolimod versus Interferon Beta-1a in Pediatric Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Treatment of patients younger than 18 years of age with multiple sclerosis has not been adequately examined in randomized trials. We compared fingolimod with interferon beta-1a in this population. METHODS: In this phase 3 trial, we randomly assigned patients 10 to 17 years of age with relapsing multiple sclerosis in a 1:1 ratio to receive oral fingolimod at a dose of 0.5 mg per day (0.25 mg per day for patients with a body weight of ≤40 kg) or intramuscular interferon beta-1a at a dose of 30 μg per week for up to 2 years. The primary end point was the annualized relapse rate. RESULTS: -weighted magnetic resonance imaging (MRI) was 4.39 with fingolimod and 9.27 with interferon beta-1a (absolute difference, 4.88 lesions; relative difference, 53%; P<0.001). Adverse events, excluding relapses of multiple sclerosis, occurred in 88.8% of patients who received fingolimod and 95.3% of those who received interferon beta-1a. Serious adverse events occurred in 18 patients (16.8%) in the fingolimod group and included seizures (in 4 patients), infection (in 4 patients), and leukopenia (in 2 patients). Serious adverse events occurred in 7 patients (6.5%) in the interferon beta-1a group and included infection (in 2 patients) and supraventricular tachycardia (in 1 patient). CONCLUSIONS: Among pediatric patients with relapsing multiple sclerosis, fingolimod was associated with a lower rate of relapse and less accumulation of lesions on MRI over a 2-year period than interferon beta-1a but was associated with a higher rate of serious adverse events. Longer studies are required to determine the durability and safety of fingolimod in pediatric multiple sclerosis. (Funded by Novartis Pharma; PARADIGMS ClinicalTrials.gov number, NCT01892722 .).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".