Nursing Management of Gastrointestinal Adverse Events Associated With Delayed-Release Dimethyl Fumarate: A Global Delphi Approach
Bibliographic record
Abstract
BACKGROUND: Gastrointestinal (GI) adverse events (AEs) are commonly encountered with delayed-release dimethyl fumarate (DMF), an approved treatment for relapsing multiple sclerosis (MS). METHODS: Two hundred thirty-nine MS nurses from 7 countries were asked to complete a 2-round Delphi survey developed by a 7-member steering committee. Questions pertained to approaches for mitigating DMF-associated GI AEs. RESULTS: Ninety-six percent of nurses followed the label recommendation for DMF dose titration in round 1, but 77% titrated the DMF dose more slowly than recommended in round 2. Although 86% of nurses advised persons with relapsing forms of MS (PWMS) to take DMF with food, patients were not routinely informed of appropriate types of food to take with DMF. Most nurses recommended both pharmacologic and nonpharmacologic symptomatic therapies for PWMS who experienced GI AEs on DMF. Pharmacologic and nonpharmacologic symptomatic therapies were regarded as equally effective at keeping PWMS on DMF. In round 2, 58% of nurses stated that less than 10% of PWMS who temporarily discontinued DMF went on to permanently discontinue treatment. Sixty-six percent of nurses stated that less than 10% of PWMS permanently discontinued DMF because of GI AEs in the first 6 months of treatment in round 1. Most nurses agreed that patient education on potential DMF-associated GI AEs contributes to adherence. CONCLUSION: This first real-world nurse-focused assessment of approaches to caring for PWMS with DMF-associated GI AEs suggests that, with implementation of slow dose titration, symptomatic therapies, and educational consultations, most PWMS can remain on DMF and, when necessary after temporary discontinuation, successfully restart DMF.
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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 | 0.126 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 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".