Meeting Report: Controlled Human Influenza Virus Infection Model Studies: Current Status and Future Directions for Innovation
Bibliographic record
Abstract
On November 13-14, 2023, the National Institute of Allergy and Infectious Diseases (NIAID) in partnership with the Task Force for Global Health, Flu Lab, the Canadian Institutes of Health Research, and the Centers for Disease Control and Prevention convened a meeting on controlled human influenza virus infection model (CHIVIM) studies to review the current research landscape of CHIVIM studies and to generate actionable next steps. Presentations and panel discussions highlighted CHIVIM use cases, regulatory and ethical considerations, innovations, networks and standardization, and the utility of using CHIVIM in vaccine development. This report summarizes the presentations, discussions, key takeaways, and future directions for innovations in CHIVIMs. Experts agreed that CHIVIM studies can be valuable for the study of influenza infection, immune response, and transmission. Furthermore, they may have utility in the development of vaccines and other medical countermeasures; however, the use of CHIVIMs to de-risk clinical development of investigational vaccines should employ a cautious approach. Endpoints in CHIVIM studies should be tailored to the specific use case. CHIVIM studies can provide useful supporting data for vaccine licensure but are not required and do not obviate the need for the conduct of field efficacy trials. Future directions in this field include the continued expansion of capacity to conduct CHIVIM studies, development of a broad panel of challenge viruses and assay reagents and standards that can be shared, streamlining of manufacturing processes, the exploration of targeted delivery of virus to the lower respiratory tract, efforts to more closely replicate natural influenza disease in CHIVIM, alignment on a definition of breadth to facilitate development of more broadly protective/universal vaccine approaches, and continued collaboration between stakeholders.
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 | 0.070 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".