Fourth Controlled Human Infection Model (CHIM) meeting – CHIMs in endemic countries, May 22–23, 2023
Bibliographic record
Abstract
Earlier meetings laid the foundations for Controlled Human Infection Models (CHIMs), also known as human challenge studies and human infection studies, including Good Manufacturing Practice (GMP) production of the challenge agent, CHIM ethics, environmental safety in CHIM, recruitment, community engagement, advertising and incentives, pre-existing immunity, and clinical, immunological, and microbiological endpoints. The fourth CHIM meeting focused on CHIM studies being conducted in endemic countries. Over the last ten years we have seen a vast expansion of the number of countries in Africa performing CHIM studies, as well as a growing number of different challenge organisms being used. Community and public engagement with assiduous ethical and regulatory oversight has been central to successful introductions and should be continued, in more community-led or community-driven models. Valuable initiatives for regulation of CHIMs have been undertaken but further capacity building remains essential.
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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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".