Controlled Human Infection Studies: Proposals for guidance on how to design, develop and produce a challenge strain
Bibliographic record
Abstract
There is an increasing need to establish quality principles for designing, developing and manufacturing challenge agents as currently these agents are classified differently by various jurisdictions. Indeed, considerations for challenge agent manufacturing vary between countries due to differences in regulatory oversight, the categorization of the challenge agent and incorporation into medicinal/vaccine development processes. To this end, a whitepaper on the guidance has been produced and disseminated for consultation to researchers, regulatory experts and regulatory or advisory bodies. This document is intended to discuss fundamental principles of selection, characterization, manufacture, quality control and storage of challenge agents for international reference. In the development phase, CMC documentation is needed for a candidate challenge agent, while standard operating procedure documentation is needed to monitor and control the manufacturing process, followed by use of qualified methods to test critical steps in the manufacturing process, or the final product itself. These activities are complementary: GMP rules, which intervene only at the time of the routine manufacturing of batches, do not contribute to the proper development and qualification of the candidate product. Some considerations regarding suitability of premises for challenge manufacturing was discussed in the presentation dedicated to "routine manufacturing".
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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 | 0.167 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.022 |
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".