Real‐World Effectiveness of Switching to Oral or Infusion Versus Injectable Disease‐Modifying Therapy in Pediatric Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To assess real-world effectiveness of switching disease-modifying therapy (DMT) in pediatric multiple sclerosis (MS) and clinically isolated syndrome (CIS) initially treated with platform injectables on disease activity. METHODS: Of 2615 pediatric-onset demyelinating disease patients at 12 clinics in the United States (US) Network of Pediatric MS Centers, those with MS/CIS on initial therapy with a platform injectable who switched to another class of platform injectable, oral or infusion DMT were analyzed. Relapse rate was modeled with negative binomial regression, adjusted for preidentified confounders. RESULTS: A total of 212 children switched DMT before age 18 (67% female, 95% MS). Ninety-three switched from injectable to injectable, 76 injectable to oral, and 43 injectable to infusion. Switchers to oral or infusion were older at onset (injectable 12.3 years, oral 13.5 years, and infusion 14.2 years) and switch (injectable 14.6 years, oral 16.0 years, and infusion 15.7 years). Switchers to infusion DMT were more likely to have enhancing lesions (injectable 45%, oral 28%, and infusion 67%). Compared to injectable (annualized relapse rate [ARR] = 0.88, 95% confidence interval [CI] = 0.52-1.48), relapse rates were lower for injectable to oral (ARR = 0.34, 95% CI = 0.20-0.57; rate ratio: 0.38, 95% CI = 0.21-0.69) and injectable to infusion (ARR = 0.18, 95% CI = 0.09-0.37; rate ratio: 0.21, 95% CI = 0.10-0.44) (p < 0.001). Adjusted number needed to treat in person-years to prevent 1 relapse with oral over injectable was 1.84 (95% CI = 1.03-8.69) and infusion over injectable 1.43 (95% CI = 1.00-3.88). INTERPRETATION: Switching from platform injectable to oral or infusion compared to other platform injectable DMT led to better disease control in pediatric MS. Long-term safety data are required. ANN NEUROL 2026;99:715-729.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".