Established disease-modifying treatments in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to summarize mechanisms of action, efficacy and safety of established disease-modifying treatments (DMTs) that have been widely approved for use in relapsing-remitting multiple sclerosis (RRMS). RECENT FINDINGS: Established and widely used DMTs for the treatment of RRMS include the interferon-β agents, glatiramer acetate, natalizumab, fingolimod, teriflunomide and dimethyl fumarate. These DMTs have quite different mechanisms of action, efficacy and safety and tolerability profiles, which are summarized concisely in the article below. SUMMARY: The treatment algorithm for RRMS is becoming increasingly complex with the ever-expanding armamentarium of DMTs. The choice of DMT will become an increasingly individual decision, based on a number of factors, including disease activity and severity, safety/tolerability profile and patient preference. Neurologists treating patients with multiple sclerosis (MS) will need a thorough knowledge of efficacy, safety and tolerability of the spectrum of DMTs available for treatment of RRMS to provide comprehensive clinical care.
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 | 0.003 | 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 | 0.001 |
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".