Effectiveness of virtual reality interactive simulation practice in prosthodontic education: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Virtual reality-based interactive simulation (VRIS) provides a safe and controlled environment for dental students and professionals to develop skills and knowledge. This study aimed to investigate the effectiveness of using the VRIS for prosthodontic practice and to explore the trends, application areas, and users' attitudes towards VRIS. MATERIALS AND METHODS: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for searching studies published until 21 March 2023 that reported quantitative or qualitative learning outcomes related to the use of VRIS for dental prosthodontic practice and clinical training. The quality of the included studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI) and Newcastle-Ottawa Scale-Education (NOS-E) tools. A random-effects meta-analysis was conducted to compare the intervention group (utilizing VRIS) and the control group (employing conventional prosthodontic training methods) based on performance skill scores and task completion time, with a significance level set at <.05. RESULTS: > 50%; p = .93). Notably, using VRIS significantly enhanced the performance scores in implant surgery practice (SMD = 0.26; 95% CI, 0.09-0.42; p < .05). Additionally, the VRIS method significantly reduced task completion time in the cavity restorative preparation task (SMD = -1.19; 95% CI, -1.85 to -0.53; p < .05). CONCLUSION: Engaging in practice with VRIS has the potential to enhance learning proficiency in prosthodontic education. The advantages associated with VRIS encompass the provision of immediate feedback, decreased task completion time, heightened confidence and motivation, accelerated skill acquisition, improved performance scores, and increased learning engagement.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".