Integrating 3Rs approaches in WHO guidelines for the batch release testing of biologicals: Reports from a series of NC3Rs stakeholder workshops
Bibliographic record
Abstract
A recent report presented to the WHO has highlighted significant opportunities for the implementation of 3Rs approaches (i.e. Replacement, Reduction and Refinement of animal tests) within their manuals, guidelines and recommendations for vaccines and biotherapeutics. The report is the culmination of a three-year project led by the UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) and co-funded by the Bill & Melinda Gates Foundation. The aim was to review the extent to which animal-based testing methods are currently described in these internationally recognised guidance documents and recommend opportunities for applying the 3Rs. International stakeholders have been engaged throughout the project to gauge opportunities and barriers to adoption of 3Rs approaches and how these vary globally, to inform the recommendations in the report. This paper summarises the output from a series of international stakeholder workshops held between March 2022 and September 2023.
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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 | 0.321 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".