Safety and tolerability of interferon beta-1b in pediatric multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Immunomodulatory therapies are widely used in adults with multiple sclerosis (MS) and safety and tolerability is well-established. Although at least 5% of all patients with MS experience the clinical onset of their disease prior to age 18 years, the available literature on safety and tolerability of immunomodulatory therapies for pediatric-onset MS is limited. METHODS: The authors retrospectively reviewed safety and tolerability of interferon beta-1b (IFNbeta-1b) in a cohort of 43 children and adolescents treated for a mean of 29.2 months (SD 22.3 months). RESULTS: Mean age at start of IFNbeta-1b treatment was 13 years. Eight children were < or =10 years. Most common adverse events included flu-like syndrome (35%), abnormal liver function test (26%), and injection site reaction (21%). No serious or unexpected adverse events were reported. CONCLUSIONS: Although data on long-term effects on the maturing organ systems are lacking, the safety profile supports the safety and tolerability of interferon beta-1b (IFNbeta-1b) in children with multiple sclerosis and related diseases. All patients treated with IFNbeta-1b should undergo regular monitoring of liver function.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".