Simulations for Surgical Training in Veterinary Medicine
Bibliographic record
Abstract
Students of veterinary medicine finishing their academic cycle show difficulty performing basic surgical techniques, which could be attributed to the few practical spaces provided in current curricula and the bioethical rules that regulate the use of animals for education and research. This study aimed to confirm a technique for surgical training in veterinary medicine, performing simulations of aural hematomas ( n = 7), removal of subcutaneous masses ( n = 7), and orchiectomies ( n = 5). Eleven cadavers were used: 4 dogs and 7 cats. The evaluation was performed by expert veterinarians who also teach veterinary surgery ( n = 7). Physical, mechanical, and organoleptic variables were analyzed. The data obtained were analyzed using multiple correspondence analysis. The results showed that the simulation for training on preserving cadavers is a valuable tool for undergraduate courses. The evaluation results were better for simulating aural hematoma and removing subcutaneous masses in dog and cat cadavers. At the same time, the surgical training of orchiectomy showed difficulties related to tissue preservation and its complex handling. Finally, it is remarked that variables, such as color and tissue resistance, are important factors to consider when preparing cadavers for simulation and surgical veterinary training; that is, the more faithful that these two aspects are to the reality of living tissues, the better the simulation experience will be.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".