A Public Records Review of Cadaver and Terminal Animal Use in US and Canadian Veterinary Schools
Bibliographic record
Abstract
Veterinary students, interns, and residents have often been taught medical and surgical skills using cadavers or terminal surgeries. However, the number of animals used by veterinary schools, their sources, and the types of procedures taught have never been quantified. In this study, active Institutional Animal Care and Use Committee protocols in which animals were euthanized prior to, during, or following training exercises were requested from public veterinary schools in the United States and Canada between December 2022 and April 2024. Protocols were evaluated for the number and species of animals requested, animal source, and types of procedures taught. Additionally, we identified seven justifications for using animals provided by principal investigators and evaluated how closely they adhered to ethical guidelines set forth by the American Association of Veterinary Medical Colleges. A total of 26 veterinary schools provided 120 Institutional Animal Care and Use Committee protocols meeting the study criteria. Equines (24/26 schools), cows (20/26), and small ruminants (19/26) were the most commonly requested species by schools, although poultry were requested in the highest numbers (8,558). Sources included client donations, commercial vendors, and university-owned animals. The most common justifications for using animals in teaching were that alternatives do not provide an equal learning experience (87/120 protocols) and that live animals are needed for students to learn nonsurgical (71/120) and surgical (65/120) procedures. There was considerable variation in how closely aligned animal use practices were to AAVMC recommendations. Limitations include probable undercounting of cadavers and the inability to verify the numbers of animals used versus requested for use.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.027 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".