Animal Experimentation in Oncology and Radiobiology: Arguments for and Against Following a Critical Literature Review
Bibliographic record
Abstract
Despite the international 3Rs principles that recommends replacing, reducing and refining the use of animals in medical experimentation, it remains difficult to obtain funding in Canada for medical research that respects these principles, particularly with regard to replacement. This observation led our team to review the literature on the arguments for and against animal experimentation in the fields of oncology and radiobiology. This article presents a synthesis of these arguments. Using the method created by McCullough and colleagues to conduct critical reviews of the ethics literature, we analysed 25 texts discussing the arguments for and against animal experimentation in oncology and radiobiology. Six broad categories of arguments for animal experimentation and eleven categories of arguments against it emerged from our analyses. Furthermore, the arguments against animal testing are more convincing from both an empirical and normative perspective. Also, most arguments obtained are transferable in other fields of medicine. In addition to the literature review, a critical reflection was conducted and other arguments were discussed. It seems that a conservative culture persists in medical research, despite the scientific evidence and ethical arguments to the contrary.
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 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 | 0.268 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".