Building Better Medicine: Translational Justice and the Quest for Equity in US Healthcare
Bibliographic record
Abstract
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.
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 | 0.117 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.111 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".