Integrating 3Rs approaches in WHO guidelines for the batch release testing of biologicals: Summary of NC3Rs final report to WHO Expert Committee for Biological Standardisation
Bibliographic record
Abstract
A recently published report from the UK National Centre for the Replacement, Refinement, and Reduction of Animals in Research (NC3Rs) has highlighted significant opportunities for the broader inclusion of 3Rs approaches (i.e. Replacement, Reduction and Refinement of animal tests) within World Health Organization (WHO) manuals, guidelines and recommendations for vaccines and biotherapeutics. The report is the culmination of a three-year project, co-funded by the Bill & Melinda Gates Foundation, to review the extent to which animal-based testing methods are described in WHO manuals, guidelines and recommendations. The aim was to identify where recommendations did not incorporate current non-animal testing strategies and/or 3Rs principles in biologicals quality control and batch release testing. The inclusion of such methods in WHO guidance documents would improve their adoption by regulators and help to accelerate the safe release of these products to the communities who need them most. The final report was presented to the WHO's Expert Committee on Biological Standardization (ECBS) in October 2023 for their consideration and response. The project findings and recommendations described in the report are summarised in this article.
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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 | 0.165 | 0.113 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".