A call to action to address critical flaws and bias in laboratory animal experiments and preclinical research
Bibliographic record
Abstract
During the design of hypothesis-driven, comparative laboratory animal experiments, investigators must control for cage effects, ensure full blinding and full randomization while adhering to established experimental designs, notably variations of the Completely Randomized Design and the Randomized Block Designs. Failure to meet these criteria introduces partial or complete confounding by multiple known and unknown variables, resulting in biased outcome measures and rendering valid statistical analysis impossible. Our analysis of a stratified, random sample of comparative laboratory animal experiments conducted in North America and Europe and published in 2022, shows that as few as 0-2.5% utilized valid, unbiased experimental designs. The failure of investigators to adopt valid, unbiased study designs undermines scientific rigour, squanders resources and animal lives, and impedes the reliable translation of preclinical research findings to human and veterinary medicine. We propose practical, achievable solutions focused on enhancing the rigour and validity of study designs. This includes developing a specialized group of scientists with expertise in the design of laboratory animal experiments and data analysis, to ensure future studies are conducted with the highest scientific standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".