117 The effect of virtual reality game preparation for the management of anxiety in children scheduled for an MRI (IMAGINE): A randomized controlled trial
Bibliographic record
Abstract
Abstract Background MRIs are known to cause anxiety and fear, especially in paediatric populations, due to claustrophobia and loud noises. This type of radiological examination requires the patient to remain still, but it can be difficult for an anxious child to follow this instruction for an extended period. Few studies have, however, examined the efficacy of virtual reality (VR) in children scheduled for an MRI. Objectives The aim of this study was to examine the efficacy of a VR program, compared to usual care, for the management of procedural-related anxiety and fear in children and adolescents scheduled for an MRI. Design/Methods A randomized controlled trial was conducted at two paediatric health centres: CHU Sainte-Justine and Maisonneuve-Rosemont Hospital in Quebec, Canada. Ninety-four (94) children from 6 to 17 years old were randomly assigned to an experimental group (VR preparation program) or a control group (information provided by a radiology technician). Anxiety was measured using the State-Trait Anxiety Inventory-Child (STAI-C) and Salivary Alpha-Amylase (SAA-Biomarker for anxiety), and fear, using the Child Fear Scale (CFS), before (T0) and after (T1) the MRI. Child and parental satisfaction were also collected after the MRI (T1). Significance level of 0.05. Results The mean difference of the STAI-C scores (T1-T0) was significantly higher in the experimental group (0.36±2.66) compared to the control group (-0.89±3.33) (p=0.011). There was no significant difference between groups on the CFS at T2 (1.4±0.8 v. 1.3±0.7; p=0.44), nor between groups on the mean difference (T1-T0) of SAA levels (-0.11±0.58 v. 0.20±1.04; p=0.10) respectively. In the experimental group, children and parental levels of satisfaction were respectively 8.43/10 (SD=2.05) and 9.17/10 (SD=1.27). The main comments received were: “VR is an excellent idea”, “I feel like it reduced my child’s and my own anxiety”, and “The game is too juvenile.” Conclusion Results have not shown a superiority of the VR preparation program over the standard procedures for the management of children’s anxiety before an MRI. Nonetheless, high satisfaction levels from both parents and children show an interest in the use of immersive technology to better prepare children for an MRI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".