A scoping review of ethical decisions and decision tools for experimental animal protocols
Bibliographic record
Abstract
BACKGROUND: Scientific research projects involving animals are required to undergo ethical evaluation, generally known as harm-benefit analysis (HBA), to ensure that they address important ethical concerns related to animal welfare and the scientific quality of the research to maximize the likelihood of their potential benefits. Research continuously shows the challenges encountered by decision-makers, prompting researchers to review how HBA is conducted and to propose tools to aid decision-making. However, the extent to which such resources are currently available, their jurisdictions of applicability, and how they guide decision-making are not entirely clear. METHOD: Through a Scoping Review methodology, a systematic literature search was conducted in PubMed, Scopus and Web of Science for publications in Europe and North America (USA and Canada) from 1985 to 2023. Title and abstract, full-text, and reference screenings, followed by data charting, respectively, were carried out for retrieved publications using pre-developed and registered review protocol. RESULTS: 17 resources that can guide HBA and decision-making were identified. They discussed what should constitute harm to animals and benefits of research, and how these two interests can be balanced to make a decision. Some adopt mathematical calculations, some propose guidelines for committee discussions, while others propose the combination of different approaches to decision-making. CONCLUSIONS: Decision-making based on deliberation among committee members should be supported over the use of scoring approaches. Additionally, making ethical decisions on a case-by-case basis is preferable to accuracy, which may not be realistically practicable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".