Use of follow-on disease-modifying treatments for multiple sclerosis: Consensus recommendations
Bibliographic record
Abstract
Background:As patents for multiple sclerosis (MS) therapies expire, follow-on disease-modifying treatments (FO-DMTs) become available at reduced cost. Concerns exist that cheaper FO-DMTs are used simply to reduce healthcare costs. However, the well-being of people with MS should take priority.Objectives:To identify best practices for FO-DMT development and use by agreeing on principles and consensus statements through appraisal of published evidence.Methods:Following a systematic review, we formulated five overarching principles and 13 consensus statements. Principles and statements were voted on by a multidisciplinary panel from 17 European countries, Argentina, Canada and the United States.Results:All principles and statements were endorsed by >80% of panellists. In brief, FO-DMTs approved within highly regulated areas can be considered effective and safe as their reference products; FO-DMTs can be evaluated case by case and do not always require Phase III trials; long-term pharmacovigilance and transparency are needed; there is lack of evidence for multiple- and cross-switching among FO-DMTs; and education is needed to address remaining concerns.Conclusion:Published data support the use of FO-DMTs in MS. The consensus may aid shared decision-making. While our consensus focused on Europe, the results may contribute to enhanced quality standards for FO-DMTs use elsewhere.
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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 | 0.094 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".