Sequencing of high-efficacy disease-modifying therapies in multiple sclerosis: perspectives and approaches
Bibliographic record
Abstract
Multiple sclerosis (MS) is characterized by chronic inflammation in conjunction with neurodegeneration within the central nervous system. Most individuals with MS begin with a relapsing remitting course that later transitions to secondary progressive MS. Currently available disease-modifying therapies (DMTs) for relapsing MS have been demonstrated to reduce disease activity, however most patients require a change in therapy over the course of their disease. Treatment goals include the prevention of relapses and disability accumulation and to achieve this objective requires careful planning. Sequencing of DMTs for individual patients should be designed in such a way to maximize disease control and minimize risk based on the mechanism of action, pharmacokinetic and pharmacodynamic properties of each therapy. This includes the DMT patients are being switched from to those they are being switched to. The reversibility of immune system effects should be a key consideration for DMT sequence selection. This feature varies across DMTs and should factor more prominently in decision making as newer treatments become available for the prevention of disability accumulation in patients with progressive MS. In this short review, we discuss the landscape of existing therapies with an eye to the future when planning for optimal DMT sequencing. While no cure exists for MS, efforts are being directed toward research in neuroregeneration with the hope for positive outcomes.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".