Twenty Years of Subcutaneous Interferon-Beta-1a for Multiple Sclerosis: Contemporary Perspectives
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic, progressive, inflammatory disorder of the central nervous system. Relapsing–remitting MS (RRMS), the most common form of the disease, is characterized by transient neurological dysfunction with concurrent accumulation of disability. Over the past three decades, disease-modifying therapies (DMTs) capable of reducing the frequency of relapses and slowing disability worsening have been studied and approved for use in patients with RRMS. The first DMTs were interferon-betas (IFN-βs), which were approved in the 1990s. Among them was IFN-β-1a for subcutaneous (sc) injection (Rebif ® ), which was approved for the treatment of MS in Europe and Canada in 1998 and in the USA in 2002. Twenty years of clinical data and experience have supported the efficacy and safety of IFN-β-1a sc in the treatment of RRMS, including pivotal trials, real-world data, and extension studies lasting up to 15 years past initial treatment. Today, IFN-β-1a sc remains an important therapeutic option in clinical use, especially around pregnancy planning and lactation, and may also be considered for aging patients, in which MS activity declines and long-term immunosuppression associated with some alternative therapies is a concern. In addition, IFN-β-1a sc is used as a comparator in many clinical studies and provides a framework for research into the mechanisms by which MS begins and progresses.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".