SARS-CoV-2 challenge studies: ethics and risk minimisation
Bibliographic record
Abstract
COVID-19 poses an exceptional threat to global public health and well-being. Recognition of the need to develop effective vaccines at unprecedented speed has led to calls to accelerate research pathways ethically, including by conducting challenge studies (also known as controlled human infection studies (CHIs)) with SARS-CoV-2 (the virus which causes COVID-19). Such research is controversial, with concerns being raised about the social, legal, ethical and clinical implications of infecting healthy volunteers with SARS-CoV-2 for research purposes. Systematic risk evaluations are critical to inform assessments of the ethics of any proposed SARS-CoV-2 CHIs. Such evaluations will necessarily take place within a rapidly changing and at times contested epidemiological landscape, in which differing criteria for the ethical acceptability of research risks have been proposed. This paper critically reviews two such criteria and evaluates whether the use of effective treatment should be a necessary condition for the ethical acceptability of SARS-CoV-2 CHIs, and whether the choice of study sites should be influenced by COVID-19 incidence levels. The paper concludes that ethical evaluations of proposed SARS-CoV-2 CHIs should be informed by rigorous, consultative and holistic approaches to systematic risk assessment.
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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 | 0.410 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".