Development and Implementation of a Basic Veterinary Ultrasound Curriculum
Bibliographic record
Abstract
Diagnostic ultrasound is an important imaging modality in veterinary medicine. Surveys of veterinarians suggest that ultrasound is a desired clinical competence and that new graduates are expected to practice basic ultrasound skills. This report describes the development and implementation of a basic ultrasound training program in the core curriculum at Iowa State University College of Veterinary Medicine (ISU-CVM). A multidisciplinary team of ISU-CVM faculty created and delivered a basic ultrasound training program consisting of two lectures and two hands-on laboratories incorporated into a second-year core course, utilizing ballistic gel and silicone phantoms as well as live-dog scanning. The focus of training was on basic image acquisition, image optimization, and regional sonographic anatomy of the canine abdomen. Students were surveyed at 6-month intervals during program implementation. Survey data from graduating students, alumni, and employers were also analyzed. The program was successfully implemented and was well-received by all key stakeholders. Alumni and employer surveys reinforced the importance of basic ultrasound skills as a competency for new graduates. Student survey data revealed that satisfaction with ultrasound training increased after implementation of the program, as did students' perception of their skill level in individual ultrasound competencies. Student surveys also identified ways to enrich the program by providing additional opportunities for ultrasound practice in subsequent laboratory courses and clinical rotations. This report summarizes lessons learned during development of basic ultrasound training in the core curriculum at ISU-CVM and can serve as a reference for other institutions considering similar programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".