Impact of simulator-based training on acquisition of transthoracic echocardiography skills in medical students
Why this work is in the frame
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Bibliographic record
Abstract
<br><b>Introduction:</b> Due to the expanding role of ultrasound as a diagnostic tool in modern medicine, medical schools rapidly include ultrasound training in their curriculum. The objective of this study was to compare simulator-based training along with classical teaching, using human models, to impart focused transthoracic echocardiography examination. <b>Subject and Methods:</b> A total of 22 medical students, with no former transthoracic echocardiography training, undertook a 90-min e-learning module, dealing with focused echocardiography and important echocardiographic pathologies. Subsequently, they had to complete a multiple-choice-questioner, followed by a 120-min practical training session either on the Heartworks™, (Cardiff, UK) and the CAE Vimedix<sup>®</sup>, (Québec, Canada) simulator (<i>n</i> = 10) or on a live human model (<i>n</i> = 12). Finally, both groups had to complete a post-test consisting of ten video-based multiple-choice-questions and a time-based, focused echocardiography examination on another human model. Two blinded expert observers scored each acquired loop which recorded 2 s of each standard view. Statistical analysis was performed with SPPS 24 (SPSS™ 24, IBM, USA) using the Mann-Whitney-Test to compare both groups. <b>Results:</b> Analysis of measurable outcome skills showed no significant difference between transthoracic echocardiography training on human models and high-fidelity simulators for undergraduate medical students. <b>Conclusions:</b> Both teaching methods are effective and lead to the intended level of knowledge and skills.<br>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it