Assessing the Different Levels of Virtual Reality That Influence Anxiety, Behavior, and Oral Health Status in Preschool Children: Randomized Controlled Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Compared with a traditional behavior management strategy and oral health training, virtual reality (VR) integrated with multisensory feedback possesses potential advantages in dentistry. OBJECTIVE: This study aimed to assess the impact of different levels of VR on anxiety, behavior, and oral health status. METHODS: This study was carried out in the Department of Pediatric Dentistry at the Tabriz University of Medical Sciences from December 2020 to June 2021. We randomly assigned 60 healthy children aged 4 years to 6 years to 4 groups, each consisting of 15 children. The study consisted of 2 consecutive sessions. During the first visit, the plaque index was calculated, and oral health education was carried out in all groups using Immersive VR (group I), Semi-immersive VR (group II), Nonimmersive VR (group III), and tell-show-do (TSD; group IV). In the second session, an amalgam restoration was performed in all groups. Participants' anxiety and behavior were recorded using the face version of the Modified Child Dental Anxiety Scale (MCDAS[f]) and Frankl scale. The plaque index was recorded in 2 follow-up sessions. RESULTS: The greatest prevalence of positive behavior (P=.004) and the lowest anxiety (P<.001) were recorded in group I, followed by group II, group III, and group IV. The plaque index scores showed a reduced trend between the first session and follow-up sessions (P<.001), but the values did not differ significantly between the 4 groups during the 3 sessions (P=.28, P=.54, P=.18). CONCLUSIONS: The most positive behavior was observed in the Immersive VR group, followed by the Semi-immersive VR, Nonimmersive VR, and TSD groups. Moreover, oral health education using VR resources can improve oral health status in children. TRIAL REGISTRATION: Iranian Registry of Clinical Trials 20210103049926N1; https://www.irct.ir/trial/53475.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".