Ethical Criteria for Human Challenge Studies in Infectious Diseases: Table 1.
Bibliographic record
Abstract
Purposeful infection of healthy volunteers with a microbial pathogen seems at odds with acceptable ethical standards, but is an important contemporary research avenue used to study infectious diseases and their treatments. Generally termed 'controlled human infection studies', this research is particularly useful for fast tracking the development of candidate vaccines and may provide unique insight into disease pathogenesis otherwise unavailable. However, scarce bioethical literature is currently available to assist researchers and research ethics committees in negotiating the distinct issues raised by research involving purposefully infecting healthy volunteers. In this article, we present two separate challenge studies and highlight the ethical issues of human challenge studies as seen through a well-constructed framework. Beyond the same stringent ethical standards seen in other areas of medical research, we conclude that human challenge studies should also include: (i) independent expert reviews, including systematic reviews; (ii) a publicly available rationale for the research; (iii) implementation of measures to protect the public from spread of infection beyond the research setting; and (iv) a new system for compensation for harm. We hope these additions may encourage safer and more ethical research practice and help to safeguard public confidence in this vital research alternative in years to come.
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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 | 0.236 | 0.350 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.024 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".