X‐Reality and the HTC Vive: Virtually No Match for the Physical Model in Anatomical Education
Bibliographic record
Abstract
Recent advancements in computer technology have resulted in the rise of X‐reality (XR) systems. Some institutions have begun to use XR systems as an alternative to cadaveric specimens and physical models. This transition has been carried out despite a lack of evidence about the efficacy of XR. In our earliest study, we compared the physical model to 3D, interactive projections on a 2D screen and found participants that learned from the physical model performed significantly better in nominal measures. Following this study, we explored the efficacy of more refined XR systems and established that participants who learned from the physical model performed significantly better in both nominal and functional measures of anatomical knowledge compared to those who learned from the Microsoft HoloLens, which is a mixed‐reality (MR) device. In our current study, we explored the efficacy of the HTC Vive, a pure virtual‐reality (VR) device, in comparison to the physical model in anatomical education. We hypothesized that given the enthusiasm surrounding this XR system, the VR model should perform at least as well as the physical model. We first conducted a preliminary qualitative study with 20 participants to develop an optimal learning environment for the VR model. Following this preliminary analysis, we recruited 20 McMaster University students with no prior formal anatomy education. Participants were allotted 10 minutes to learn 20 anatomical structures from a pelvic model on the VR headset. Participants were then given a 25‐question test on a female, cadaveric pelvis with no time limit. The test questions were either nominal or functional. The nominal questions involved identifying the structures labelled on the cadaver and the functional questions involved interpreting the function of a structure based on its location and form. We compared the results of the VR participants to the 20 participants who learned on the physical model and the 20 participants who learned on the MR model from our HoloLens study. Our analysis demonstrated that participants learning on the physical model performed significantly better than the VR model when comparing total testing scores (56.4% vs 45.0%, respectively; p = 0.034). Furthermore, the VR model participants performed better than the MR model participants in terms of total testing score, although these results were not statistically significant (45.0% vs 37.6%, respectively; p = 0.267). In conclusion, these findings provide further evidence to support the superiority of the physical model over XR systems. Our future directions involve testing with other, more complex anatomical structures and exploring the factors that contribute to the superiority of the physical model, such as the role of stereopsis. Support or Funding Information Self‐funded This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".