Virtual Reality Bell‐Ringer: The Development and Testing of a Stereoscopic Application for Human Gross Anatomy
Bibliographic record
Abstract
As post-secondary education migrates online, developing and evaluating new avenues for assessment in anatomy is paramount. Three-dimensional (3D) visualization technology is one area with the potential to augment or even replace resource-intensive cadaver use in anatomical education. This manuscript details the development of a smartphone application, entitled "Virtual Reality Bell-Ringer (VRBR)," capable of displaying monoscopic two-dimensional (2D) or stereoscopic 3D images with the use of an inexpensive cardboard headset for use in spot examinations. Cadaveric image use, creation, and pinning processes are explained, and the source code is provided. To validate this tool, this paper compares traditional laboratory-based spot examination assessment stations against those administered using the VRBR application to test anatomical knowledge. Participants (undergraduate, n = 38; graduate, n = 13) completed three spot examinations specific to their level of study, one in each of the modalities (2D, 3D, laboratory) as well as a mental rotation test (MRT), Stereo Fly stereotest, and cybersickness survey. Repeated measures ANCOVA suggested participants performed significantly better on laboratory and 3D stations compared to 2D stations. Moderate to severe cybersickness symptoms were reported by 63% of participants in at least one category while using the VRBR application. Highest reported symptoms included: eye strain, general discomfort, difficulty focusing, and difficulty concentrating. Overall, the VRBR application is a promising tool for its portability, affordability, and accessibility. Due to reported cybersickness and other technical limitations, the use of VRBR as an alternative to cadaveric specimens presents several challenges when testing anatomy knowledge that must be addressed before widespread adoption.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".