Development of a virtual anatomy lab (VanVR App) for implementation during the Covid19 pandemic to ensure 3D learning with scanned prosections
Bibliographic record
Abstract
Many anatomy curricula rely on access to gross anatomy labs so that students can engage in experiential learning with donated human bodies. Without access to an anatomy lab, learning is limited to study with 2D print or online materials and 3D models – both physical and virtual. When the restrictions due to the Covid19 pandemic were put into place and it became clear that learning would not occur on campus and in our labs, we wanted to ensure that the students still had access to 3‐dimensional learning from prosections. We built on our experience in 3D photogrammetry and 3D scanning and integrated these specimens into a custom‐built 3D virtual anatomy lab environment. The aim of the design was to create a sense of space and professionalism in the virtual experience. The VanVR application was created using Unity Engine and WebGL with 3D scanning/photogrammetry, and programmed for use on desktop browsers to increase accessibility. The goal of this application is not only to show 3D specimens, but also create an anatomy one‐stop teaching and study tool that includes related reference material for each specimen, such labelled images, videos, web modules, etc. Within this virtual space, students can rotate, zoom and compare specimens, which would be difficult to do during an in‐person anatomy class. The VanVR app has been the main anatomy teaching tool at the University of British Columbia during the pandemic lockdown. Once in‐person teaching resumes, this app will be available as a reference tool for study and review. Feedback from the students about their learning experience has been positive and the VanVR app proved to be a good approach to anatomy education for remote and online learning.
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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 | 0.000 |
| 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".