Animal testing for vaccines. Implementing replacement, reduction and refinement: challenges and priorities
Bibliographic record
Abstract
Transition to in vitro alternative methods from in vivo in vaccine release testing and characterization, the implementation of the consistency approach, and a drive towards international harmonization of regulatory requirements are most pressing needs in the field of vaccines. It is critical for global vaccine community to work together to secure effective progress towards animal welfare and to ensure that vaccines of ever higher quality can reach the populations in need in the shortest possible timeframe. Advancements in the field, case studies, and experiences from Low and Middle Income Countries (LMIC) were the topics discussed by an international gathering of experts during a recent conference titled "Animal Testing for Vaccines - Implementing Replacement, Reduction and Refinement: Challenges and Priorities". This conference was organized by the International Alliance for Biological Standardization (IABS), and held in Bangkok, Thailand on December 3 and 4 2019. Participants comprised stakeholders from many parts of the world, including vaccine developers, manufacturers and regulators from Asia, Europe, North America, Australia and New Zealand. In interactive workshops and vibrant panel discussions, the attendees worked together to identify the remaining barriers to validation, acceptance and implementation of alternative methods, and how harmonization could be promoted, especially for LMICs.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".