Developing a Patient-Directed Policy Framework for Managing Orphan and Ultra-Orphan Drugs Throughout Their Lifecycle
Bibliographic record
Abstract
INTRODUCTION: Policy decisions related to orphan and ultra-orphan drugs challenge traditional decision-making processes and often frustrate those affected by them. In general, these drugs are associated with significant uncertainties around clinical benefit, 'value for money', affordability, and 'adoption/diffusion', all of which arise from a lack of available high-quality evidence. Increasingly, patients with rare diseases and their families are looking for opportunities to contribute to initiatives aimed at reducing these uncertainties. Therefore, a policy framework for guiding their involvement is needed to optimize the impact of any evidence generated. OBJECTIVES: The aims of this study were (1) to explore opportunities for patient involvement in reducing decision uncertainties throughout the lifecycle of orphan and ultra-orphan drugs from the perspectives of patients within the Canadian rare disease community; and (2) to develop a policy framework for patient input that maximizes the impact of their involvement on decision uncertainties around orphan and ultra-orphan drugs. METHODS: Two one-day conferences and four workshops involving patients and/or families from rare disease communities in Canada were held to discuss issues around orphan and ultra-orphan drug development, access, and coverage, and identify opportunities for patient input to reduce related decision uncertainties. Their feedback and the findings from a recent literature review on patient involvement in rare diseases were combined into a draft policy framework based upon Kingdon's multiple streams model of decision making. The framework was presented to a group of patients and other stakeholders, including providers, pharmaceutical drug plan managers, and industry representatives, and then revised accordingly. RESULTS: Patients and family members/caregivers identified tangible ways of contributing to the generation of information at all stages of the drug lifecycle. However, the proximity of that information to the reduction of a specific decision uncertainty varied. While the scope of possible ways mentioned was less broad when compared with the findings of the literature review, the focus was similar-capturing the clinical benefit of an orphan or ultra-orphan drug. A policy framework comprising three stages, each with a key question and corresponding set of sub-questions to be asked by patients, was developed. The three main sequential questions were as follows. (1) What uncertainties need to be addressed? (2) What roles should patients play? (3) Is each role feasible? CONCLUSIONS: Reducing decision uncertainties around orphan and ultra-orphan drugs requires a policy framework that explicates when and what type of information needs to be generated, and recognizes the role of patients as important sources of such information throughout the lifecycle of these drugs.
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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 | 0.157 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".