De-escalating and discontinuing disease-modifying therapies in multiple sclerosis
Bibliographic record
Abstract
The development of disease-modifying therapies (DMTs) for the treatment of multiple sclerosis (MS) has been highly successful in recent decades. It is now widely accepted that early initiation of DMTs after disease onset is associated with a better long-term prognosis. However, the question of when and how to de-escalate or discontinue DMTs remains open and critical. This topic was discussed during an international focused workshop organized by the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) in 2023. The aim was to review the current evidence on the rationale for, and the potential pitfalls of, treatment de-escalation in MS. Several clinical scenarios emerged, mainly driven by a change in the benefit-risk ratio of DMTs over the course of the disease and with ageing. The workshop also addressed the issue of de-escalation by the type of DMT used and in specific situations, including pregnancy and paediatric onset MS. Finally, we provide practical guidelines for selecting appropriate patients, defining de-escalation and monitoring modalities and outlining unmet needs in this field.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".