Emerging Therapies in Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
Disease modifying therapy (DMT) first became available for relapsing-remitting multiple sclerosis (RRMS) fifteen years ago with the development of the moderately effective injectable agents interferon (IFN)-beta and glatiramer acetate (GA). The subsequent licensure of mitoxantrone (MX) and natalizumab (NZ) has allowed for better control of refractory disease at the expense of potentially life-threatening side effects in a minority of patients. This dichotomy between DMT potency and safety also characterizes the next generation of DMTs. Five oral medications (fingolimod, cladribine, teriflunomide, laquinimod and fumarate) are at various stages of phase III trials and it is anticipated that at least some of these will be on the market within the next year. The development of oral agents would be a tremendous advance with respect to convenience and it is anticipated that this would dramatically increase the number of patients on therapy. In parallel with oral therapies, powerful immunosuppressive monoclonal antibodies (alemtuzumab, rituximab/ocrelizumab, daclizumab) are also being evaluated. Enthusiasm for the next generation of therapies is tempered by safety concerns. Serious and occasionally fatal complications have occurred with the emerging monoclonal therapies and rigorous patient selection will be required for these agents. Moreover, some of the oral DMTs that are most eagerly awaited by patients have also been associated with serious side-effects in the trials to date. It is unclear how oral agents will be incorporated into future treatment algorithms given the need to weigh the ease of oral administration against the relative inconvenience but long-term safety of current first-line injectable therapies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".