Digital Dissection with CT Scans Enhances Anatomy Education: First‐year Medical Students’ Perception of Radiology Teaching During the Covid‐19 Pandemic
Bibliographic record
Abstract
OBJECTIVE The aim of this study was to evaluate students’ perceptions of the educational value of online, interactive anatomy radiology laboratories. HYPOTHESIS Digital dissection is performed with computed tomography (CT) scans on near life‐size anatomy visualization tables and has been used to teach radiology anatomy at our institution. These sessions were pivoted to online delivery in response to COVID‐19. With the ongoing movement towards technology integration in medical education, if home access to these online laboratories is made available then we hypothesize the perceived learning experience should be preserved. METHODS 290 first‐year medical students participated in 10 weekly digital radiology anatomy laboratories, which employed 3D CT scans to emphasize the clinical applications of anatomy. Students reviewed the cases at home prior to the lab on a mobile platform and labs were delivered synchronously via the Zoom platform in a facilitated large‐group format. After the course, a voluntary anonymous online survey was distributed assessing learner satisfaction, task value, emotional achievement, and cognitive load. RESULTS Survey response rate was 44%. Most participants reported the labs improved their knowledge of anatomy (82%), disease (81%) and clinical decision making (65%), including surgical knowledge (76%). The large group, synchronous learning format was found to be the most effective (68%). Positive emotional achievement was reported (mean 2.98). The extraneous cognitive load of the at‐home mobile technology and lack of time were perceived as the greatest programmatic challenges. CONCLUSION Synchronous digital dissection labs were perceived as a valuable addition to the first‐year medical curriculum, enhancing learners’ clinical decision making and preparation for medical sub‐specialties. This format of radiology anatomy education can be integrated into blended learning environments to provide students with additional learning opportunities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".