Integrated virtual and cadaveric dissection laboratories enhance first year medical students’ anatomy experience: a pilot study
Bibliographic record
Abstract
BACKGROUND: Radiology integration into medical anatomy courses is well established, but there is a paucity of literature on integrating virtual dissection into cadaveric dissection laboratories. Virtual dissection is the digital dissection of medical images on touchscreen anatomy visualization tables. The purpose of this pilot study was to investigate the feasibility of integrating virtual dissection into a first-year medical cadaver-based anatomy course and to assess students' overall attitude towards this new technology. METHODS: All students in first-year medicine at a single medical school participated in this study (n = 292). Six virtual dissection laboratories, which focused on normal anatomy, were developed and integrated into a cadaver-based anatomy course. The virtual dissection table (VDT) was also integrated into the final anatomy spot exam. Following the course, students completed a short evidence-informed survey which was developed using a theoretical framework for curriculum evaluation. Numerical data were tabulated, and qualitative content analysis was performed on students' unstructured comments. RESULTS: The survey response rate was 69.2% (n = 202/292). Most (78.7%) students reported that virtual dissection enhanced their understanding of the cadaveric anatomy and the clinical applications of anatomy. Most (73.8%) students also felt that the VDT was an effective use of the laboratory time. Thirteen narrative comments were collected, most of which (61.5%) identified strengths of the curriculum. CONCLUSIONS: In this pilot study, students perceived that their learning was enhanced when virtual dissection was combined with a cadaver-based anatomy laboratory. This study demonstrates that there is potential for virtual dissection to augment cadaveric dissection in medical education.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".