Ending the evidence gap for pregnancy, HIV and co‐infections: ethics guidance from the PHASES project
Bibliographic record
Abstract
INTRODUCTION: While pregnant people have been an important focus for HIV research, critical evidence gaps remain regarding prevention, co-infection, and safety and efficacy of new antiretroviral therapies in pregnancy. Such gaps can result in harm: without safety data, drugs used may carry unacceptable risks to the foetus or pregnant person; without pregnancy-specific dosing data, pregnant people face risks of both toxicity and undertreatment; and delays in gathering evidence can limit access to beneficial next-generation drugs. Despite recognition of the need, numerous barriers and ethical complexities have limited progress. We describe the process, ethical foundations, recommendations and applications of guidance for advancing responsible inclusion of pregnant people in HIV/co-infections research. DISCUSSION: The 26-member international and interdisciplinary Pregnancy and HIV/AIDS: Seeking Equitable Study (PHASES) Working Group was convened to develop ethics-centred guidance for advancing timely, responsible HIV/co-infections research with pregnant people. Deliberations over 3 years drew on extensive qualitative research, stakeholder engagement, expert consultation and a series of workshops. The guidance, initially issued in July 2020, highlights conceptual shifts needed in framing research with pregnant people, and articulates three ethical foundations to ground recommendations: equitable protection from drug-related risks, timely access to biomedical advances and equitable respect for pregnant people's health interests. The guidance advances 12 specific recommendations, actionable within the current regulatory environment, addressing multiple stakeholders across drug development and post-approval research, and organized around four themes: building capacity, supporting inclusion, achieving priority research and ensuring respect. The recommendations describe strategies towards ethically redressing the evidence gap for pregnant people around HIV and co-infections. The guidance has informed key efforts of leading organizations working to advance needed research, and identifies further opportunities for impact by a range of stakeholder groups. CONCLUSIONS: There are clear pathways towards ethical inclusion of pregnant people in the biomedical research agenda, and strong agreement across the HIV research community about the need for - and the promise of - advancing them. Those who fund, conduct, oversee and advocate for research can use the PHASES guidance to facilitate more, better and earlier evidence to optimize the health and wellbeing of pregnant people and their children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".