Canine Prostate Palpation Simulator as a Teaching Tool in Veterinary Education
Bibliographic record
Abstract
Adult dogs, especially elderly ones, are commonly affected by prostate diseases. Performing rectal palpation during physical examination in dogs is important in small animal clinical diagnosis. Prostate palpation training allows students to learn how to correctly introduce the finger into the rectum and identify the location, size, symmetry, and consistency of the prostate. Alternative methods are needed to teach this technique without using live dogs. Thus, our aim was to develop a canine prostate palpation simulator to provide students with the opportunity to learn the prostate palpation technique in dogs and to assess their opinion of this simulator as a teaching tool. The inner part of the canine mannequin contains a rotation system with three types of prostates that can be exchanged during the exam. Of the 64 participating students, 81% had never used alternative methods and 92.2% had never performed any prostatic palpation. According to the students' opinions, performing a clinical examination on a simulator allowed them to be prepared and familiarized with the palpation technique. They felt satisfied learning a practical method in a harmless way. Both the 3R (replacement, reduction, and refinement) and dog welfare principles were present in most of the students' concerns. We conclude that the simulator can help students to develop clinical skills for prostate palpation in dogs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".