Development and Integration of Models for Teaching Ram Breeding Soundness Examinations in Veterinary Education
Bibliographic record
Abstract
Proficiency with ram breeding soundness examinations requires competency with palpation, a skill that can be difficult to teach and assess. There are limited small ruminant clinical skills models available, despite the advantages they offer in veterinary education. We developed reusable models for teaching ram breeding soundness examinations, focusing on scrotal assessment and palpation. Then we integrated these models into a practical session where multiple clinical aspects were included. We created anatomically normal ("sound") testes using 3D modeling software before editing these to display common abnormalities ("unsound" testes). Then, we 3D printed two-part molds and cast the silicone testes. Testes were inserted into siliconized, lubricated stockings facilitating free movement during palpation. Scrotal sacs were sewn from polar fleece and suspended to mimic natural orientation in a live, standing ram. As well as for scheduled classes, we used the models as a station in our course's Objective Structured Clinical Examination (OSCE) assessment. Our models offer advantages in the veterinary education context. Their relatively low cost and durability facilitates their classification as "open access" within our skills lab for student deliberate practice outside scheduled classes. They provide a uniform student learning experience that does not rely on live animals or clinical case load and aligns with best-practice recommendations from accrediting bodies. Student engagement and OSCE outcomes were good, but going forward it would be ideal to collaborate with a program that uses live rams for teaching and assessing this skill to directly examine the impact of our models on confidence and competence.
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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 | 0.003 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".