From Justification to Legitimacy: A Deliberative Framework for Decisions Around Expensive Drugs for Rare Diseases
Bibliographic record
Abstract
Decisions about expensive drugs for rare diseases (EDRDs) raise complex ethical challenges beyond the allocation of limited healthcare resources. This paper examines the ethical dimensions of EDRD decision-making, arguing that the framing of such decisions as simply ethical or unethical is inadequate. In complex healthcare systems characterized by diversity and inequality, no single normative theory provides an incontrovertible solution. EDRD decisions require both ethical justification (grounded in carefully interpreted and balanced values) and ethical legitimacy (achieved through fair processes that respect autonomy). Interest-based accounts of procedural justice are insufficient because they mischaracterize how people form identities and interests. Deliberative democratic approaches that engage multiple perspectives through reflective, inclusive processes are more promising, though they face challenges of complexity, time constraints, and resistance to transparency. Transparency is essential, and courageous leadership is needed to establish processes that accommodate diverse perspectives while addressing the practical realities of healthcare systems. Such leadership can help create ethically defensible EDRD decisions that balance patient needs with system sustainability.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".