Hand-Held Point-of-Care Ultrasound: A New Tool for Veterinary Student Self-Driven Learning in the Time of COVID-19
Bibliographic record
Abstract
The coronavirus pandemic abruptly halted all in-person clerkships, or clinical rotations, for clinical veterinary students across the United States. Online clerkships in radiology offered the opportunity to expand the student's ability to interpret medical images but did not allow for the development of physical hands-on imaging skills recognized as core competencies in veterinary medicine. The present report highlights the value of providing veterinary students with a smartphone-associated Butterfly iQ point-of-care ultrasound during a 3-week self-driven virtual clerkship. During the virtual rotation, the student was able to develop the skills required to generate sufficient quality images using three horses residing on her property. The affordability, portability, ease of use of the Butterfly iQ and availability of animals made it possible to develop hands-on imaging skills when distance learning was required.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".