Design and Implementation of a Safe Equine Radiation-Free Radiographic Simulator for Veterinary Skills Training in the Pre-clinical Curriculum
Bibliographic record
Abstract
The ninth standard of accreditation as outlined by the American Veterinary Medical Association (AVMA) Council on Education (COE) mandates that all accredited colleges of veterinary medicine must provide instruction in medicine and surgery, including principles of practice, hands-on experiences with diagnostic methods, and interpretation of clinical findings. In equine practice, diagnostic imaging is used as a first-line diagnostic approach and is an integral part of pre-purchase and lameness examinations. Accuracy and consistency of radiographic interpretation relies heavily upon procedural techniques to acquire diagnostic images and overcome both motion artifacts and obliquity. Because the sizes and temperaments of equine patients potentiate hazardous working conditions for the veterinary team, learners might benefit from simulations that allow them to practice holding the x-ray generator and the imaging plate for diagnostic image acquisition in the absence of live horses. This teaching tip describes the development of a novel equine radiographic simulator for skills training in the veterinary curriculum. The model allows learners to handle imaging equipment safely and without radiation exposure as they develop proficiency positioning radiographic plates and placing directional markers. Learners can also test their understanding of radiographic positioning in reverse: if given a radiograph, they can be asked to describe how the x-ray generator was positioned to obtain the diagnostic image. Future iterations will investigate the simulator's efficacy with respect to learning outcomes when the model is paired with an assessment rubric as part of an objective standardized clinical examination.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".