Impact of simulator-based training on acquisition of transthoracic echocardiography skills in medical students
Bibliographic record
Abstract
Introduction: 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. Subject and Methods: 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®, (Québec, Canada) simulator (n = 10) or on a live human model (n = 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. Results: Analysis of measurable outcome skills showed no significant difference between transthoracic echocardiography training on human models and high-fidelity simulators for undergraduate medical students. Conclusions: Both teaching methods are effective and lead to the intended level of knowledge and skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".