Global Peginterferon Beta-1a Tolerability Management Best Practices: A Nurse-Focused Delphi Approach
Bibliographic record
Abstract
INTRODUCTION: Injection site reactions (ISRs) and flu-like symptoms (FLS) are common in patients with relapsing forms of multiple sclerosis (MS) treated with peginterferon beta-1a. The purpose of this Delphi analysis was to explore peginterferon beta-1a discontinuation rates across MS treatment centers, to obtain consensus on effective mitigation and management strategies for ISRs and FLS, and to identify areas where additional training and education for nurses and patients could improve treatment outcomes. METHODS: In this modified Delphi process, an international steering committee of eight MS-certified nurses developed two rounds of surveys, which were completed by 262 and 188 MS nurses, respectively, representing nine countries. RESULTS: On average, nurses reported that 25% and 30% of patients treated with peginterferon beta-1a experienced ISRs and FLS, respectively. Discontinuation due to severe ISRs or FLS was most common in the first 6 months of treatment, yet follow-up visits typically took place 6 months after peginterferon beta-1a initiation. Preferred management strategies for ISRs included nonsteroidal anti-inflammatory drugs and rotation of the injection site, whereas preferred management strategies for FLS included acetaminophen/paracetamol and hydration/nutrition. Most nurses (77%) agreed that additional education and training on ISR and FLS management would bolster their confidence in treating patients with these symptoms. CONCLUSION: Delphi respondents reached consensus on ISR and FLS management strategies, which can help to inform treatment decisions. The results of this global Delphi analysis indicate that management of ISRs and FLS could be improved with more frequent follow-up visits and individualized training and education.
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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 | 0.122 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 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".