Value-Based Reimbursement Decisions for Orphan Drugs: A Scoping Review and Decision Framework
Bibliographic record
Abstract
BACKGROUND: The rate of development of new orphan drugs continues to grow. As a result, reimbursing orphan drugs on an exceptional basis is increasingly difficult to sustain from a health system perspective. An understanding of the value that societies attach to providing orphan drugs at the expense of other health technologies is now recognised as an important input to policy debates. OBJECTIVES: The aim of this work was to scope the social value arguments that have been advanced relating to the reimbursement of orphan drugs, and to locate these within a coherent decision-making framework to aid reimbursement decisions in the presence of limited healthcare resources. METHODS: A scoping review of the peer reviewed and grey literature was undertaken, consisting of seven phases: (1) identifying the research question; (2) searching for relevant studies; (3) selecting studies; (4) charting, extracting and tabulating data; (5) analyzing data; (6) consulting relevant experts; and (7) presenting results. The points within decision processes where the identified value arguments would be incorporated were then located. This mapping was used to construct a framework characterising the distinct role of each value in informing decision making. RESULTS: The scoping review identified 19 candidate decision factors, most of which can be characterised as either value-bearing or 'opportunity cost'-determining, and also a number of value propositions and pertinent sources of preference information. We were able to synthesize these into a coherent decision-making framework. CONCLUSION: Our framework may be used to structure policy discussions and to aid transparency about the values underlying reimbursement decisions for orphan drugs. These values ought to be consistently applied to all technologies and populations affected by the decision.
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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 | 0.204 | 0.293 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.066 | 0.049 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.012 | 0.006 |
| 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".