Delphi panel to understand the real-world occurrence and management of ofatumumab injection-related reactions among healthcare providers of people with relapsing multiple sclerosis
Bibliographic record
Abstract
Background: People with relapsing forms of multiple sclerosis (PwRMS) treated with ofatumumab, a fully human anti-CD20 monoclonal antibody, can experience local/systemic injection-related reactions (IRRs). However, data on the occurrence and management of local/systemic IRRs in real-world clinical settings are limited. Objective: This study aimed to better understand clinicians' perspectives regarding occurrence and management of local/systemic IRRs among PwRMS treated with ofatumumab in clinical practice. Methods: A panel of US-based neurologists and advanced practice providers experienced with ofatumumab therapy in PwRMS participated in a three-round online modified Delphi study. In round 1, participants completed a demographics survey and Delphi questionnaire on IRR management. In round 2, they attended a live webinar to obtain feedback on round 1 results. In round 3, they reviewed round 1 and 2 feedback and provided their final responses. Results: = 4) completed all three rounds. Participants strongly agreed that local/systemic IRRs, regardless of severity, were unlikely with ofatumumab. Pre-/post-treatment of systemic IRRs was not uniformly required. Conclusion: This study gives health care providers insight into the potential occurrence and management of IRRs with ofatumumab in the clinical practice setting.
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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 | 0.070 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".