The challenge of human challenge research models: A Canadian perspective
Bibliographic record
Abstract
Research in which healthy volunteers are exposed to pathogens or other aetiologic agents that may cause disease remains controversial. Proponents suggest such work is key to understanding pathways of infection and the efficacy of vaccines and treatments. Yet, this research creates ethical and legal issues surrounding consent, participant vulnerability and the potential for harm. Moreover, public trust in research could be compromised if avoidable, serious harm occurs, making challenge research risky. Among Canadian research ethics guidelines, overarching messages are that participant interests cannot be subservient to those of research and that risks must be proportional to likely benefits. Moreover, common law fiduciary obligations to clinical research participants and the deterrent effect of potential tortious or criminal negligence act to reinforce the idea that challenge protocols should be a strategy of last resort. Researchers could benefit from clear guidance directly addressing the unique issues with challenge research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.025 | 0.081 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 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".