The Use of Ultrasound Simulators to Strengthen Scanning Skills in Medical Students: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: This study evaluates the use of ultrasound simulators for retaining and improving ultrasound skills acquired in undergraduate ultrasound training. METHODS: Fourth-year medical students (n = 19) with prior training in point-of-care sonography for shock assessment were recruited for this study. Students were randomly assigned to a study group (n = 10) that followed an undergraduate ultrasound training curriculum, then used a simulator to complete 2 self-directed practice ultrasound sessions over 4 weeks. The control group (n = 9) followed the same undergraduate ultrasound training curriculum and received no additional access to a simulator or ultrasound training. A blinded assessment of the students was performed before and after the 4-week study period to evaluate their image acquisition skills on standardized patients (practical examination). To evaluate the student's clinical understanding of pathological ultrasound images, students watched short videos of prerecorded ultrasound scans and were asked to complete a 22-point questionnaire to identify their findings (visual examination). RESULTS: All results were adjusted to pretest performance. The students in the study group performed better than those in the control group on the visual examination (80.1% versus 58.9%; P = .003) and on the practical examination (77.7% versus 57.0%; P = .105) after the 4-week study period. The score difference on the postintervention practical examinations was significantly better for the study group compared to the control group (11.6% versus -9.9%; P = .0007). CONCLUSION: The use of ultrasound simulators may be a useful tool to help previously trained medical students retain and improve point-of-care ultrasound skills and knowledge.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".