Validation of a Novel Ultrasound Simulation Model for Teaching Foundation-Level Ultrasonography Skills to Veterinary Students
Bibliographic record
Abstract
Veterinary ultrasonography is a complex, advanced skill requiring repetitive exposure and supervision to gain competence. Consequently, newly graduated veterinarians are underprepared and lack the resources to achieve basic ultrasound proficiency upon graduation. Ultrasound simulation has been proposed as an adjunct educational tool for teaching entry-level ultrasound skills to student veterinarians. The objectives of this multicentric prospective observational cohort study were to describe the development of a novel ultrasound training model, establish model construct and face validity, and seek participant feedback. The model was constructed using three-dimensional silicone shapes embedded in ballistics gel within a glass container. A novice cohort of 15 veterinary students and 14 expert participants were prospectively enrolled in the study. Each cohort underwent training and assessment phases using a simulation model. Participants were asked to (a) determine shape location, (b) identify shape type using a shape bank, and (c) measure shape axes using the caliper tool. Time for each phase was recorded. Anonymous post-participation survey feedback was obtained. For most shapes (4/6), experts performed significantly better than novices in identifying shape type and location. Generally, no significant difference was found in mean axis shape measurements between cohorts or compared to the true mean axis measurements. No significant difference was found in scan time for either phase. This study's results support the validation of this ultrasound simulation model and may demonstrate early evidence for its use as a training tool in the veterinary curriculum to teach entry-level ultrasound skills.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".