Virtual reality simulation of panoramic radiographic anatomy for dental students
Bibliographic record
Abstract
PURPOSE: This work aimed to utilize virtual reality (VR) in dental radiographic anatomical interpretation in junior dental students and test if it can enhance student learning, engagement, and performance. METHODS: VR software for panoramic anatomy was developed. Sixty-nine first-year dental students were divided into a control group (lecture-based) and an experimental group (VR) to learn panoramic radiographic anatomy. Both groups were then tested on knowledge via a 20-question quiz. Student feedback on VR experience was collected via an online survey. RESULTS: There was a statistically significant difference between lecture-based and VR students in the correct identification of anatomical landmarks. Lecture-based students scored higher in identifying the ear lobe, hyoid bone, condylar neck, and external oblique ridge, whereas VR students scored higher in identifying zygoma (Chi-squared test, p < 0.005). The VR group reported high evaluation on all perception items of the online feedback survey on their experience (Student t-test, p < 0.005). CONCLUSIONS: Lecture-based students generally showed better performance in panoramic radiographic anatomy. Several structures were not correctly identified in both groups of novice students. The positive feedback of VR experience encourages future implementation in education to augment conventional methods of radiographic anatomy in dentistry with considerations to repeated exposures throughout undergraduate dental 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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".