Efficacy and safety of subcutaneous interferon-β-1a in patients with a first demyelinating event and early multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: Multiple sclerosis (MS) is an inflammatory demyelinating disease of the CNS. Evidence suggests that MS should be treated as early as possible in order to maximize the benefit of treatment. AREAS COVERED: This review details current understanding about the treatment of relapsing-remitting MS (RRMS). The pharmacological and clinical data on the use of subcutaneous (s.c.) interferon β-1a (IFN-β-1a) as a therapeutic option for RRMS are covered, with a focus on the importance of treating patients with MS as early as possible in the course of the disease, in order to delay permanent axonal damage that is responsible for the signs and symptoms of disease progression. EXPERT OPINION: There is a wealth of data on the treatment of RRMS with s.c. IFN-β-1a indicating that patients treated during the early inflammatory stages of the disease have significantly improved short-term outcomes compared with patients who commence treatment late. It remains to be determined whether the short-term effects of early treatment will translate into long-lasting benefits, although it is hoped that ongoing research will help to answer this question.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".