Evaluation of a Self-Driven Large Animal Point of Care Ultrasound Learning Program for Veterinary Students
Bibliographic record
Abstract
Point of care ultrasound (POCUS) has the potential to improve health care outcomes and is increasingly used in veterinary primary care and specialty practice. The aim of this study was to evaluate a self-driven POCUS learning program during clinical rotations in a large animal teaching hospital. A randomized controlled trial of 94 students evaluated the hypotheses that access to a self-driven POCUS program would improve scores on a written test and the quality of subjective, objective, assessment and plan (SOAP) documents. Use of the POCUS devices and perceptions of veterinary students toward POCUS were analyzed. The self-driven POCUS learning program was feasible, and the perception of most students (94%) was that the program was useful for their education. Access to equipment, ability to scan individually, the hands-on learning aspect, and clinicians' help were the most valued aspects of the program. Earlier access to POCUS in the curriculum, hands-on tutorials/labs, and a more structured learning program were identified by students as aspects to improve. Access to the self-driven POCUS program resulted in significantly higher scores on the written test in a subpopulation of students with lower scores. No significant differences in results on the multiple-choice tests in the subpopulation with higher scores or in SOAP scores of the complete group or subpopulations were found. A self-driven POCUS learning program was perceived as beneficial by students, increased POCUS knowledge in students with lower test scores, and was possible to implement in a busy large animal teaching hospital.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".