Current international trends in the treatment of multiple sclerosis in children—Impact of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Only recently has the first disease-modifying therapy been approved for children with multiple sclerosis (MS) and practice patterns including substantial off-label use have evolved. Understanding attitudes towards treatment of paediatric MS and whether this has changed due to the ongoing COVID-19 pandemic is vital to guide future therapeutic trials and for developing guidelines that reflect practice. METHODS: We performed an online survey within the International Paediatric Multiple Sclerosis Study Group between July and September 2020. The survey was sent to 130 members from 25 countries and consisted of five sections: demographic data, treatment, disease modifying therapies and COVID-19, outcome and three patient cases. RESULTS: The survey was completed by 66 members (51%), both paediatric neurologists and adult neurologists. Fingolimod and β-interferons were the most frequently used disease-modifying therapies, especially among paediatric neurologists. Almost a third (31%) of respondents had altered their prescribing practice due to COVID-19, in particular at the beginning of the pandemic. CONCLUSIONS: The survey results indicate a tendency of moving from the traditional escalation therapy starting with injectables towards an early start with newer, highly effective disease modifying therapies. The COVID-19 pandemic only slightly affected prescribing patterns and treatment choices in paediatric MS.
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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 | 0.004 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".