Novel and imminently emerging treatments in relapsing–remitting multiple sclerosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize mechanisms of action, efficacy, and safety of novel and imminently emerging disease-modifying treatments (DMTs) intended to be used in relapsing-remitting multiple sclerosis (RRMS). RECENT FINDINGS: Novel and imminently emerging DMTs for the treatment of RRMS include alemtuzumab, daclizumab, ocrelizumab, pegylated interferon-β-1a, and three times weekly glatiramer acetate. These DMTs have substantially different mechanisms of action, efficacy, and safety and tolerability profiles, which are summarized concisely in this article. SUMMARY: The treatment landscape of RRMS is evolving rapidly as the available treatment options have doubled in recent years, and a number of novel DMTs will likely become available in the near future. Choosing the optimal DMT for patients is becoming an increasingly complex process, and the care of patients with MS will likely require regular input from neurologists subspecializing in the care of patients with MS. As the use of novel DMTs with unknown long-term safety profiles increases, postmarketing surveillance and vigilance with regards to safety monitoring will be essential to confirm the safety and clinical efficacy of these DMTs for patients with RRMS.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.004 | 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".