A randomized controlled study on medical students learning anatomy through hands‐on ultrasound
Bibliographic record
Abstract
Anatomy education in the undergraduate medical curriculum faces many competing interests, including the increasing demand for ultrasound learning. Instead of being treated as a separate subject, ultrasound can offer a unique lens to visualize anatomy without using cadaveric materials. This study used a randomized controlled design to compare learning with hands-on ultrasound to learning on cadavers. Forty preclinical medical students were randomized to an experimental group or a control group. Both groups attended a session covering structures in the neck, upper limb, and abdomen. The experimental group learned using hands-on ultrasound imaging, while the control group was taught on a dissected cadaver. Participants completed multiple-choice tests on anatomical relationships and structure identification at four time points: presession, post-session, 1-week follow-up, and 1-month follow-up. There was no statistical difference between test scores of the two groups. The post-session average score (54% for cadaver group, 57% for ultrasound group) more than doubled the presession average score (20% for cadaver group, 25% for ultrasound group) (p < 0.001). One-week follow-up and 1-month follow-up scores (40%-44%) significantly decreased from immediate post-session for both groups. Eighteen of the 20 ultrasound-facilitated participants felt more confident operating an ultrasound device compared to before the session. Three quarters of all participants agreed hands-on ultrasound should be part of their anatomy education. This study shows hands-on ultrasound imaging can effectively substitute cadaver-based learning of certain anatomy topics at the introductory level. Ultrasound integration highlights the clinical relevance of anatomy and provides an innovative tool for anatomical education.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".