Worldwide Trends in the Use of Animals in Research: The Contribution of Genetically-modified Animal Models
Bibliographic record
Abstract
The Three Rs--Reduction, Replacement and Refinement--which were first proposed in 1959 by Russell and Burch, have become widely accepted principles in the governance of humane animal research. However, there is substantial variation in the ways in which different countries document the numbers and types of research animals used, making it difficult to determine how effectively the Three Rs are being implemented. Here, we provide the first data illustrating worldwide trends in animal use for research purposes. To document global trends in animal use, we sampled 2691 articles from 24 countries, published between 1983 and 2007, in four scientific journals. We show that the percentage of articles reporting animal use has risen in the past 15 years. The rising popularity of genetic modification methods has contributed to this trend: reported genetically-modified animal use has more than doubled since 1997. We also show that mice are the most commonly-used species for genetic modification, and that, even in 2007, relatively inefficient random integration methods were still widely used to achieve genetic modification. These results illustrate shortcomings in the effort to implement the Three Rs in animal research.
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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 | 0.088 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".