Advances in the treatment of relapsing–remitting multiple sclerosis: the role of pegylated interferon β-1a
Bibliographic record
Abstract
Kendra L Furber,1–3 Marina Van Agten,1–3 Charity Evans,2,4 Azita Haddadi,2 J Ronald Doucette,3–5,† Adil J Nazarali1–4 1Laboratory of Molecular Cell Biology, 2College of Pharmacy and Nutrition, 3Neuroscience Research Cluster, University of Saskatchewan, 4Cameco Multiple Sclerosis Neuroscience Research Center, City Hospital, 5Department of Anatomy and Cell Biology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada †Dr. J Ronald Doucette passed away on May 15, 2016 Abstract: Multiple sclerosis (MS) is a progressive, neurodegenerative disease with unpredictable phases of relapse and remission. The cause of MS is unknown, but the pathology is characterized by infiltration of auto-reactive immune cells into the central nervous system (CNS) resulting in widespread neuroinflammation and neurodegeneration. Immunomodulatory-based therapies emerged in the 1990s and have been a cornerstone of disease management ever since. Interferon β (IFNβ) was the first biologic approved after demonstrating decreased relapse rates, disease activity and progression of disability in clinical trials. However, frequent dosing schedules have limited patient acceptance for long-term therapy. Pegylation, the process by which molecules of polyethylene glycol are covalently linked to a compound, has been utilized to increase the half-life of IFNβ and decrease the frequency of administration required. To date, there has been one clinical trial evaluating the efficacy of pegylated IFN. The purpose of this article is to provide an overview of the role of IFN in the treatment of MS and evaluate the available evidence for pegylated IFN therapy in MS. Keywords: interferon, pegylation, multiple sclerosis, relapsing–remitting, disease-modifying therapy
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".