Sequencing of high-efficacy disease-modifying therapies in multiple sclerosis: perspectives and approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it