Ethically designing research to inform multidimensional, rapidly evolving policy decisions: Lessons learned from the PROMISE HIV Perinatal Prevention Trial
Bibliographic record
Abstract
Research in rapidly evolving policy contexts can lead to the following ethical challenges for sponsors and researchers: the study's standard of care can become different than what patients outside the study receive, there may be political or other pressure to move ahead with unproven interventions, and new findings or revised policies may decrease the relevance of ongoing studies. These ethical challenges are considerable, but not unprecedented. In this article, we review the case of a multinational, randomized, controlled perinatal HIV prevention trial, the "PROMISE" (Promoting Maternal Infant Survival Everywhere) study. PROMISE compared the relative efficacy and safety of interventions to prevent mother to child transmission of HIV. The sponsor engaged an independent international ethics panel to address controversy about the study's standard of care and relevance as national and international guidelines changed. This ethics panel concluded that continuing the PROMISE trial as designed was ethically permissible because: (1) participants in all arms received interventions that were effective, and there was insufficient evidence about whether one intervention was more effective or safer than the other, and (2) data from PROMISE could be useful for a diverse range of stakeholders. In general, trials designed to inform rapidly evolving policy issues should develop mechanisms to revisit social value while recognizing that the value of research varies for diverse stakeholders with legitimate reasons to weigh evidence differently. We conclude by providing four reasons that trials may depart from the standard of care after a change in policy, while remaining ethically justifiable, and by suggesting how to improve existing trial oversight mechanisms to address evolving social value.
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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 | 0.764 | 0.749 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.027 | 0.039 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".