Evaluating the Therapeutic Potential of Ublituximab in the Treatment of MS: Design, Development and Place in Therapy
Bibliographic record
Abstract
Sarah-Jane Martin,1,2 Melanie Guenette,1 Jiwon Oh1 1Division of Neurology, Department of Medicine, St Michael’s Hospital, Toronto, Canada; 2University of Glasgow, Glasgow, UKCorrespondence: Jiwon Oh, Email jiwon.oh@unityhealth.toAbstract: B cells are critical to the pathogenesis of multiple sclerosis (MS), an autoimmune disease of the central nervous system. B cell depletion using anti-CD20 monoclonal antibodies (mAbs) has proven to be an extremely successful treatment strategy, with profound suppression of both clinical and radiological evidence of focal inflammatory disease. Several anti-CD20 mAbs are now licensed for use in MS, with ublituximab being the latest to gain regulatory approval. The unique properties of each of the anti-CD20 mAb may result in nuanced differences in timing, duration and depth of B cell depletion, with the potential for such differences to have a clinical relevance to both drug efficacy and adverse effects. In this review, we summarize the design, development, and current place in MS therapy for ublituximab.Keywords: multiple sclerosis, ublituximab, B-cell therapy, anti-CD20 monoclonal antibody
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".