Can medical learners achieve point-of-care ultrasound competency using a high-fidelity ultrasound simulator?: a pilot study
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound (PoCUS) is currently not a universal component of curricula for medical undergraduate and postgraduate training. We designed and assessed a simulation-based PoCUS training program for medical learners, incorporating image acquisition and image interpretation for simulated emergency medical pathologies. We wished to see if learners could achieve competency in simulated ultrasound following focused training in a PoCUS protocol. METHODS: Twelve learners (clerks and residents) received standardized training consisting of online preparation materials, didactic teaching, and an interactive hands-on workshop using a high-fidelity ultrasound simulator (CAE Vimedix). We used the Abdominal and Cardiothoracic Evaluation by Sonography (ACES) protocol as the curriculum for PoCUS training. Participants were assessed during 72 simulated emergency cardiorespiratory scenarios. Their ability to complete an ACES scan independently was assessed. Data was analyzed using R software. RESULTS: Participants independently generated 574 (99.7%) of the 576 expected ultrasound windows during the 72 simulated scenarios and correctly interpreted 67 (93%) of the 72 goal-directed PoCUS scans. CONCLUSIONS: Following a focused training process using medical simulation, medical learners demonstrated an ability to achieve a degree of competency to both acquire and correctly interpret cardiorespiratory PoCUS findings using a high-fidelity ultrasound simulator.
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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 | 0.006 | 0.017 |
| 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 | 0.001 |
| 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 | 0.001 |
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".