HIV Pre-Exposure Prophylaxis (PrEP)—A Quantitative Ethics Appraisal
Bibliographic record
Abstract
BACKGROUND: There is now strong evidence that preventive oral antiretroviral therapy can moderately reduce likelihood of HIV infection. This concept is called HIV pre-exposure prophylaxis (PrEP). Premature closures of some previous PrEP clinical trials, secondary to ethical concerns, did not stop research. We aimed to appraise the extent of ethics considerations reporting in PrEP study documents. METHODS: We conducted a systematic quantitative ethics appraisal, grounded in PrEP literature and using eight principles proposed by Ezechiel Emanuel. We developed an a priori checklist of 101 evidence-based ethics items. We obtained protocols for eleven of nineteen clinical controlled studies identified. Two reviewers independently appraised study documents against the checklist. Ethics appraisal was synthesized using adjusted percentages of items reported. RESULTS: On average, 58% of the 101 ethics items were mentioned or addressed in documents, with variations noted both across studies and across principles. Considerations pertaining to social value were least reported (43% of checklist items, on average) whereas considerations related to informed consent and favorable risk-benefit ratio were most reported (75% of checklist items, on average). DISCUSSION: Some PrEP studies reportedly address more ethics considerations than others but, overall, ethics considerations reporting could be much improved. While this review does not allow us to comment on the actual execution of HIV PrEP trials, it is a reminder that optimism generated by potentially effective interventions should not overshadow the importance of ethics in research design and development. Improving ethics reporting might improve the perceived value of PrEP research and subsequent data.
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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 | 0.466 | 0.663 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".