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Record W4412065151 · doi:10.1186/s12909-025-07553-5

Influence of virtual reality simulation (excluding augmented reality) on endodontic learning experiences of undergraduate dental students: a systematic review

2025· review· en· W4412065151 on OpenAlexaboutno aff
Muhammad Qasim Javed, Bilal Arjumand, Shaul Hameed Kolarkodi, Sundus Atique, Ayman M. Abulhamael, Muhammad Sohail Zafar

Bibliographic record

VenueBMC Medical Education · 2025
Typereview
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsnot available
FundersAjman University
KeywordsVirtual realityMedical educationAugmented realityPsychologyDentistryMedicineComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Virtual Reality (VR) technology has demonstrated a promising prospect for enhancing endodontic learning in undergraduate dental students by boosting their procedural skills, accuracy, and confidence. AIM: To systematically evaluate the effectiveness of virtual reality (VR) simulation in endodontic education among undergraduate dental students, with a specific focus on four key outcomes: procedural accuracy, enhancement of student confidence, reduction in procedural errors, and overall learner satisfaction. METHODS: An exhaustive literature search was carried out in December 2024 in PubMed, Cochrane Library, Embase, Scopus, and ClinicalTrials.gov. Randomized controlled trials (RCTs), quasi-experimental studies, and cross-sectional studies published between 2010 and 2024 were included in the review. Risk of bias was appraised as follows: Cochrane Risk of Bias 2.0 (RoB2) tool for RCTs; Newcastle-Ottawa Quality Assessment Scale adapted for cross-sectional studies; National Institute of Health (NIH) Quality Assessment Tool for before-and-after studies; and the Methodological Index for Non-Randomized Studies (MINORS) tool for non-randomized studies without a comparator group. RESULTS: Fifteen studies were included in the final analysis. VR-based training showed statistically significant differences between the pre and post-test scores regarding procedural accuracy and efficiency for tasks at the end of endodontics. These results showed that VR training leads to greater confidence and skill levels in students than traditional approaches, improved retention of knowledge, and a reduction in errors. Advantages notwithstanding, limitations around cost and accessibility were observed. CONCLUSION: VR simulation is an effective, valuable tool in the endodontic education toolbox. Further studies should assess cost-effectiveness and long-term clinical performance effects.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.088
GPT teacher head0.495
Teacher spread0.407 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2025
Admission routes1
Has abstractyes

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