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Record W2915988192 · doi:10.2196/11684

A Free Virtual Reality Experience to Prepare Pediatric Patients for Magnetic Resonance Imaging: Cross-Sectional Questionnaire Study

2019· article· en· W2915988192 on OpenAlex
Jonathan Ashmore, Jerome Di Pietro, Kelly Williams, Euan Stokes, Anna Symons, Martina Smith, Louise Clegg

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2019
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realityMagnetic resonance imagingAnxietyTest (biology)Cross-sectional studyMedicineMedical physicsMedical emergencyPsychologyComputer scienceRadiologyPsychiatryHuman–computer interactionPathology

Abstract

fetched live from OpenAlex

BACKGROUND: A magnetic resonance image (MRI) is a diagnostic test that requires patients to lie still for prolonged periods within a claustrophobic and noisy environment. This can be difficult for children to tolerate, and often general anesthetic (GA) is required at considerable cost and detriment to patient safety. Virtual reality (VR) is a newly emerging technology that can be implemented at low cost within a health care setting. It has been shown to reduce fear associated with a number of high-anxiety situations and medical procedures. OBJECTIVE: The goal of the research was to develop a VR resource to prepare pediatric patients for MRI, helping to reduce anxieties in children undergoing the procedure. METHODS: A freely accessible VR preparation resource was developed to prepare pediatric patients for their upcoming MRI. The resource consists of an app and supporting preparation book and used a series of panoramic 360 degree videos of the entire MRI journey, including footage from within the bore of the scanner. The app, deployed via the Android Play Store and iOS App Store, can be viewed on most mobile phones, allowing a child to experience an MRI in VR using an inexpensive Google Cardboard headset. The app contains 360 degree videos within an animated, interactive VR interface designed for 4 to 12-year-olds. The resource was evaluated as part of a clinical audit on 23 patients (aged 4 to 12 years), and feedback was obtained from 10 staff members. In 5 patients, the resource was evaluated as a tool to prepare patients for an awake MRI who otherwise were booked to have an MRI under GA. RESULTS: The VR preparation resource has been successfully implemented at 3 UK institutions. Of the 23 patients surveyed, on a scale of 1 to 10, the VR resource was rated with a median score of 8.5 for enjoyment, 8 for helpfulness, and 10 for ease of use. All patients agreed that it made them feel more positive about their MRI, and all suggested they would recommend the resource to other children. When considering their experiences using the resource with pediatric patients, on a scale of 1 to 10, the staff members rated the VR resource a median score of 8.5 for enjoyment, 9 for helpfulness, and 9 for ease of use. All staff believed it could help prepare children for an awake MRI, thus avoiding GA. A successful awake MRI was achieved in 4 of the 5 children for whom routine care would have resulted in an MRI under GA. CONCLUSIONS: Our VR resource has the potential to relieve anxieties and better prepare patients for an awake MRI. The resource has potential to avoid GA through educating the child about the MRI process.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.328
Teacher spread0.308 · 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