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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".