The Development of a Virtual Reality Simulation to Reduce Anxiety in Pediatric Patients Undergoing Magnetic Resonance Imaging
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is a valuable diagnostic tool in pediatric care, offering high-resolution images without ionizing radiation; however, the unfamiliar environment, confined space, and loud noises may cause distress, claustrophobia, and anxiety. This may lead to an increased need for sedation, and longer imaging times. At Victoria Children's Hospital in London, Ontario, the Child Life program currently uses a doll and MRI model to help reduce anxiety in pediatric patients through a learning-based approach. While these methods are effective, they are limited in their ability to provide a realistic representation of the MRI experience. To address this, we developed a gamified MRI experience designed specifically for pediatric patients at Victoria Children's Hospital using virtual reality (VR), a computer-generated 3D environment experienced through a headset. The simulation replicates the sights, sounds, and spatial aspects of the MRI environment, helping children to familiarize themselves with the experience beforehand. While our VR room is a replica of the real MRI room in Victoria Children's Hospital, it could be adapted for use in other facilities by modifying the environmental details. Future work will evaluate the simulation's effectiveness in reducing anxiety through quantitative and qualitative assessments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".