Ethics of Controlled Human Infection Studies With Hepatitis C Virus
Bibliographic record
Abstract
Global elimination of hepatitis C virus (HCV) will be difficult to attain without an effective HCV vaccine. Controlled human infection (CHI) studies with HCV were not considered until recently, when highly effective treatment became available. However, now that successful treatment of a deliberate HCV infection is feasible, it is imperative to evaluate the ethics of establishing a program of HCV CHI research. Here, we evaluate the ethics of studies to develop an HCV CHI model in light of 10 ethical considerations: sufficient social value, reasonable risk-benefit profile, suitable site selection, fair participant selection, robust informed consent, proportionate compensation or payment, context-specific stakeholder engagement, fair and open collaboration, independent review and oversight, and integrated ethics research. We conclude that it can be ethically acceptable to develop an HCV CHI model. Indeed, when done appropriately, developing a model should be a priority on the path toward global elimination of HCV.
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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 | 0.475 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".