Enhancing involvement of people with multiple sclerosis in clinical trial design
Bibliographic record
Abstract
BACKGROUND: Although often overlooked, patient and public involvement (PPI) is vital when considering the design and delivery of complex and adaptive clinical trial designs for chronic health conditions such as multiple sclerosis (MS). METHODS: We conducted a rapid review to assess current status of PPI in the design and conduct of clinical trials in MS over the last 5 years. We provide a case study describing PPI in the development of a platform clinical trial in progressive MS. RESULTS: We identified only eight unique clinical trials that described PPI as part of articles or protocols; nearly, all were linked with funders who encourage or mandate PPI in health research. The OCTOPUS trial was co-designed with people affected by MS. They were central to every aspect from forming part of a governance group shaping the direction and strategy, to the working groups for treatment selection, trial design and delivery. They led the PPI strategy which enabled a more accessible, acceptable and inclusive design. CONCLUSION: Active, meaningful PPI in clinical trial design increases the quality and relevance of studies and the likelihood of impact for the patient community. We offer recommendations for enhancing PPI in future MS clinical trials.
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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 | 0.404 | 0.502 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".