What risks should be permissible in controlled human infection model studies?
Bibliographic record
Abstract
Controlled human infection model (CHIM) studies involve the intentional exposure of healthy research volunteers to infectious agents. These studies contribute to knowledge about the cause or development of disease and to the advancement of vaccine research. But they also raise ethical questions about the kinds of risks that should be permissible and whether limits should be imposed on research risks in CHIM studies. Two possible risk thresholds have been considered for CHIM studies. The first suggests constraining ethically permissible risks according to a minimal risk threshold and the second endorses a higher risk threshold that excludes irreversible or fatal infections. I argue that neither of these thresholds is persuasive and situate questions about risk thresholds in CHIM studies within a broader debate about permissible risks in research. I argue that risks in CHIM studies should be constrained according to limits on research risks that do not offer corresponding benefits in all studies rather than developing a unique risk threshold for CHIM studies. I then propose five recommendations for the ethical assessment of risk in CHIM studies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.321 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".