Immersive Reality–Based Training Simulator for Dental Extraction: Protocol for a Randomized Pilot Trial
Bibliographic record
Abstract
BACKGROUND: Dental students' competencies are shaped by their training, yet traditional methods with mannequins often lack the depth necessary for comprehensive understanding, potentially impacting clinical proficiency. Immersive reality (IR) innovatively offers interactive and scenario-based environments that may enhance skill acquisition. OBJECTIVE: This study evaluates the effectiveness of IR-based training implementation in comparison with conventional training methods for dental extractions. METHODS: A prospective multicenter randomized clinical trial was conducted. Students were randomized to either IR-based training on open and closed extractions or conventional hands-on tutorials by oral surgeons. Post training, participants' satisfaction and understanding were assessed and analyzed. RESULTS: As of September 2025, 60 students from Hasanuddin University, Makassar, and Padjajaran University, Bandung, have been enrolled, and study enrollment will be expanded to Universitas Sumatera Utara, Medan. Data collection is ongoing and will conclude in November 2025, with expected dissemination in early 2026. CONCLUSIONS: IR-based training offers a novel approach that may boost motivation, knowledge retention, and skill transfer in dental education. This pilot protocol explores IR's feasibility and potential to advance dental students' competencies. TRIAL REGISTRATION: Indonesian Clinical Research Registry INA-QES4CC5; https://ina-crr.kemkes.go.id/en/studi/207. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/74978.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.010 |
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".