Subcutaneous interferon β-1a in pediatric patients with multiple sclerosis: Regional differences in clinical features, disease management, and treatment outcomes in an international retrospective study
Bibliographic record
Abstract
BACKGROUND: To further understand management of pediatric patients with multiple sclerosis (MS), we examined disease features, clinical practice patterns, and response to treatment in the United States (US) and seven other countries ('rest of World'; ROW). METHODS: Anonymized data, recorded as part of routine clinical practice, were obtained from medical records (1997-2009) of study participants (who received subcutaneous interferon β-1a before age 18 years) from the US and ROW. Samples were stratified by age (preadolescents [<12 years] and adolescents [12-17 years]). RESULTS: US adolescents had a higher mean body mass index versus ROW adolescents (BMI; 27.2 versus 22.5 kg/m(2)), started disease-modifying therapy (DMT) earlier after the first relapse, were more likely to have received a DMT before initiating subcutaneous interferon β-1a, had a higher relapse rate, and were more likely to switch from subcutaneous interferon β-1a to another DMT before the end of the observation period. CONCLUSIONS: This retrospective analysis of a multinational sample of pediatric MS patients who received subcutaneous interferon β-1a found that those from the US had higher BMI, relapsed more frequently, and were managed differently, compared with ROW patients. Future prospective studies are needed to confirm these observations and ascertain their clinical significance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".