Evaluation of Two Training Methods for Teaching the Abdominal Focused Assessment with Sonography for Trauma Technique (A-FAST) to First- and Second-Year Veterinary Students
Bibliographic record
Abstract
Ultrasound techniques, including focused assessment with sonography for trauma (FAST) examinations, are commonly used in veterinary practice, making inclusion of ultrasound in veterinary curricula increasingly important. The best approach for teaching ultrasound techniques in veterinary medicine has not been evaluated. This study compared the results of two training techniques, live-animal training and online video instruction, on students' performance during abdominal FAST (A-FAST) examinations. Thirty-eight first- and second-year veterinary students were randomly assigned to learn A-FAST via a live-animal laboratory or an instructional video. The live-animal group received one-on-one instruction in A-FAST techniques during a single laboratory. The video group received a link to an instructional video demonstrating A-FAST techniques, allowing unlimited viewing opportunities over a two-week period. Both groups were also provided written instructional information. All participants were assessed on their ability to find and correctly name the four A-FAST quadrants on a live animal. We found a significant difference between the two groups in the students' ability to identify the diaphragmatic-hepatic (DH) view, but for the other three views (hepatorenal, splenorenal, and cystocolic), training method did not affect performance. Results suggest the potential for using a multi-modal instructional approach to teach ultrasound techniques to veterinary students.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".