Introduction of Ultrasound Simulation in Medical Education: Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Ultrasound is ubiquitous across all disciplines of medicine; it is one of the most commonly used noninvasive, painless diagnostic tools. However, not many are educated and trained well enough in its use. Ultrasound requires not only theoretical knowledge but also extensive practical experience. The simulated setting offers the safest environment for health care professionals to learn and practice using ultrasound. OBJECTIVE: This study aimed to (1) assess health care professionals' need for and enthusiasm toward practicing using ultrasound via simulation and (2) gauge their perception and acceptance of simulation as an integral element of ultrasound education in medical curricula. METHODS: A day-long intervention was organized at the American University of Beirut Medical Center (AUBMC) to provide a free-of-charge interactive ultrasound simulation workshop-using CAE Vimedix high-fidelity simulator-for health care providers, including physicians, nurses, ultrasound technicians, residents, and medical students. Following the intervention, attendees completed an evaluation, which included 4 demographic questions and 16 close-ended questions based on a Likert scale agree-neutral-disagree. The results presented are based on this evaluation form. RESULTS: A total of 41 participants attended the workshop (46% [19/41] physicians, 30% [12/41] residents, 19% [8/41] sonographers, and 5% [2/41] medical students), mostly from AUBMC (88%, 36/41), with an average experience of 2.27 (SD 3.45) years and 30 (SD 46) scans per attendee. Moreover, 15 out of 41 (36%) participants were from obstetrics and gynecology, 11 (27%) from internal medicine, 4 (10%) from pediatrics, 4 (10%) from emergency medicine, 2 (5%) from surgery and family medicine, and 5 (12%) were technicians. The majority of participants agreed that ultrasound provided a realistic setting (98%, 40/41) and that it allowed for training and identification of pathologies (88%, 36/41). Furthermore, 100% (41/41) of the participants agreed that it should be part of the curriculum either in medical school or residency, and most of the participants approved it for training (98%, 40/41) and teaching (98%, 40/41). CONCLUSIONS: All attendees were satisfied with the intervention. There was a positive perception toward the use of simulation for training and teaching medical students and residents in using ultrasound, and there was a definite need and enthusiasm for its integration into curricula. Simulation offers an avenue not only for teaching but also for practicing the ultrasound technology by both medical students and health care providers.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".