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Record W4407897183 · doi:10.1080/15265161.2025.2457713

Building Better Medicine: Translational Justice and the Quest for Equity in US Healthcare

2025· article· en· W4407897183 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe American Journal of Bioethics · 2025
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversity of Toronto
FundersNational Human Genome Research Institute
KeywordsTranslational medicineTechnocracyHealth careEconomic JusticeEquity (law)Engineering ethicsNormativeTranslational researchTranslational sciencePolitical sciencePublic relationsMedicineSociologyPoliticsEngineeringLawSocial science

Abstract

fetched live from OpenAlex

Despite considerable scientific progress and the evolution of regulatory pathways to ensure safety and efficacy, US healthcare continues to see increasing health disparities. This suggests that clinical translation in of itself cannot be the only measure of its own success, especially when the most marginalized patients, are neglected in the development and implementation of medical innovations. This raises the question of whether a system that is narrowly focused on technical achievement can meet the moral obligations of medicine and public health. We argue that traditional technocratic standards are failing to integrate normative considerations into biomedical translation. What is needed is a translational domain that moves beyond safety and efficacy toward anticipating how proposed technologies will be effective in society as it exists. We propose an additional metric of success: translational justice.

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.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.089
GPT teacher head0.473
Teacher spread0.384 · 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