Comparing Smartphone Virtual Reality Exposure Preparation to Care as Usual in Children Aged 6 to 14 Years Undergoing Magnetic Resonance Imaging: Protocol for a Multicenter, Observer-Blinded, Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: A magnetic resonance imaging (MRI) procedure can cause preprocedural and periprocedural anxiety in children. Psychosocial interventions are used to prepare children for the procedure to alleviate anxiety, but these interventions are time-consuming and costly, limiting their clinical use. Virtual reality (VR) is a promising way to overcome these limitations in the preparation of children before an MRI scan. OBJECTIVE: The objective of this study is (1) to develop a VR smartphone intervention to prepare children at home for an MRI procedure; and (2) to examine the effect of the VR intervention in a randomized controlled trial, in which the VR intervention will be compared to care as usual (CAU). CAU involves an information letter about an MRI examination. The primary outcome is the child's procedural anxiety during the MRI procedure. Secondary outcomes include preprocedural anxiety and parental anxiety. We hypothesize that the VR preparation will result in a higher reduction of the periprocedural anxiety of both parents and children as compared to CAU. METHODS: The VR intervention provides a highly realistic and child-friendly representation of an MRI environment. In this randomized controlled trial, 128 children (aged 6 to 14 years) undergoing an MRI scan will be randomly allocated to the VR intervention or CAU. Children in the VR intervention will receive a log-in code for the VR app and are sent cardboard VR glasses. RESULTS: The VR smartphone preparation app was developed in 2020. The recruitment of participants is expected to be completed in December 2022. Data will be analyzed, and scientific papers will be submitted for publication in 2023. CONCLUSIONS: The VR smartphone app is expected to significantly reduce pre- and periprocedural anxiety in pediatric patients undergoing an MRI scan. The VR app offers a realistic and child-friendly experience that can contribute to modern care. A smartphone version of the VR app has the advantage that children, and potentially their parents, can get habituated to the VR environment and noises in their own home environment and can do this VR MRI preparation as often and as long as needed. TRIAL REGISTRATION: ISRCTN Registry ISRCTN20976625; https://www.isrctn.com/ISRCTN20976625. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41080.
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 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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".