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Record W4417209518 · doi:10.1002/jimd.70126

From Justification to Legitimacy: A Deliberative Framework for Decisions Around Expensive Drugs for Rare Diseases

2025· review· en· W4417209518 on OpenAlexaff
Bashir Jiwani

Bibliographic record

VenueJournal of Inherited Metabolic Disease · 2025
Typereview
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsFraser Health
Fundersnot available
KeywordsFraming (construction)LegitimacyNormativeTransparency (behavior)Health careEquity (law)AccountabilityDeliberationFace (sociological concept)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.434
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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