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 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 | 0.788 | 0.833 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.015 | 0.011 |
| Research integrity | 0.040 | 0.058 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".