Report of the third conference on next-generation sequencing for adventitious virus detection in biologics for humans and animals
Bibliographic record
Abstract
Next-generation sequencing (NGS) has been proven to address some of the limitations of the current testing methods for adventitious virus detection in biologics. The International Alliance for Biological Standardization (IABS), the U.S. Food and Drug Administration (FDA), and the European Directorate for the Quality of Medicines and Healthcare (EDQM) co-organized the "3rd Conference on Next-generation Sequencing for Adventitious Virus Detection in Biologics for Humans and Animals", which was held on September 27-28, 2022, in Rockville, Maryland, U.S.A. The meeting gathered international representatives from regulatory and public health authorities and other government agencies, industry, contract research organizations, and academia to present the current status of NGS applications and the progress on NGS standardization and validation for detection of viral adventitious agents in biologics, including human and animal vaccines, gene therapies, and biotherapeutics. Current regulatory expectations were discussed for developing a scientific consensus regarding using NGS for detection of adventitious viruses. Although there are ongoing improvements in the NGS workflow, the development of reference materials for facilitating method qualification and validation support the current use of NGS for adventitious virus detection.
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 | 0.028 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".