Virtual Dissection Adds Educational Value to a Traditional Medical Undergraduate Cadaveric Anatomy Course
Bibliographic record
Abstract
Background Virtual dissection is performed on near life‐size anatomy visualization tables (AVTs), which are like hospital radiology workstations. Patient CT scans are loaded into these tables and through powerful software interactions students work together to manipulate the data and perform their dissection. The purpose of this study was to develop virtual dissection laboratories for first year medical students and to qualitatively assess the educational value of these sessions as well as students' preferred pedagogical approaches for this new technology. Methods All students in the first‐year medical undergraduate program were included in this study (n = 292). A Basic Virtual Dissection Curriculum focused on normal anatomy was offered to all students concurrently with the Cadaveric Laboratory Sessions and an Advanced Virtual Dissection Curriculum focused on pathology was offered as 4 extra‐curricular sessions. 36.6% of students participated in the Advanced Curriculum. Following the course, both groups of students were surveyed to determine their attitude toward virtual dissection and the pedagogical approaches they perceived to be the most useful for this technology. Results were statistically analyzed using the Schulze method. Results The response rate for the Basic Curriculum was 69.2% and the response rate for the Advanced Curriculum was 82.9%. 93% indicated that virtual dissection was “definitely” a valuable addition to the anatomy lab. 89% of respondents “agreed” or “strongly agreed” that AVT virtual dissection improved their understanding of disease and pathology. They reported that the aortic aneurysm case was the most memorable case because the imaging made it easier to understand the pathogenesis of the disease. Students felt that small group demonstration and problem‐based learning would be the best teaching approaches for this technology. Conclusions Virtual dissection adds educational value to undergraduate anatomy teaching, primarily because it provides students with a clinical context for the anatomy they are learning. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".