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Record W2896123415 · doi:10.2196/10902

A Novel Clinician-Orchestrated Virtual Reality Platform for Distraction During Pediatric Intravenous Procedures in Children With Hemophilia: Randomized Controlled Trial

2018· article· en· W2896123415 on OpenAlexvenueno aff
Amy L. Dunn, Jeremy Patterson, Charmaine Biega, Alice Grishchenko, John Luna, Joseph Stanek, Robert Strouse

Bibliographic record

VenueJMIR Serious Games · 2018
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNationwide Children's HospitalOhio State UniversityNational Hemophilia Foundation
KeywordsDistractionMedicineRandomized controlled trialPopulationUsabilityAnxietyClinical endpointPhysical therapyMedical emergencySurgeryPsychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Needles are frequently required for routine medical procedures. Children with severe hemophilia require intensive intravenous (IV) therapy to treat and prevent life-threatening bleeding and undergo hundreds of IV procedures. Fear of needle-related procedures may lead to avoidance of future health care and poor clinical outcomes. Virtual reality (VR) is a promising distraction technique during procedures, but barriers to commercially available VR platforms for pediatric health care purposes have prevented widespread use. OBJECTIVE: We hypothesized that we could create a VR platform that would be used for pediatric hemophilia care, allow clinician orchestration, and be safe and feasible to use for distraction during IV procedures performed as part of complex health care. METHODS: We created a VR platform comprising wireless, adjustable, disposable headsets and a suite of remotely orchestrated VR games. The platform was customized for a pediatric hemophilia population that required hands-free navigation to allow access to a child's hands or arms for procedures. A hemophilia nurse observing the procedure performed orchestration. The primary endpoint of the trial was safety. Preliminary feasibility and usability of the platform were assessed in a single-center, randomized clinical trial from June to December 2016. Participants were children with hemophilia aged 6-18 years. After obtaining informed consent, 25 patients were enrolled and randomized. Each subject, 1 caregiver, and 1 hemophilia nurse orchestrator assessed the degree of preprocedural nervousness or anxiety with an anchored, combined modified visual analog (VAS)/FACES scale. Each participant then underwent a timed IV procedure with either VR or standard of care (SOC) distraction. Each rater assessed the distraction methods using the VAS/FACES scale at the completion of the IV procedure, with questions targeting usability, engagement, impact on procedural anxiety, impact on procedural pain, and likability of the distraction technique. Participants, caregivers, and nurses also rated how much they would like to use VR for future procedures. To compare the length of procedure time between the groups, Mann-Whitney test was used. RESULTS: Of the 25 enrolled children, 24 were included in the primary analysis. No safety concerns or VR sickness occurred. The median procedure time was 10 (range 1-31) minutes in the VR group and was comparable to 9 (range 3-20) minutes in the SOC group (P=.76). Patients in both the groups reported a positive influence of distraction on procedural anxiety and pain. Overall, in 80% (34/45) of the VR evaluations, children, caregivers, and nurses reported that they would like to use VR for future procedures. CONCLUSIONS: We demonstrated that an orchestrated, VR environment could be developed and safely used during pediatric hemophilia care for distraction during IV interventions. This platform has the potential to improve patient experience during medical procedures. TRIAL REGISTRATION: Clinical Trials.gov NCT03507582; https://clinicaltrials.gov/ct2/show/NCT03507582 (Archived by WebCite at http://www.webcitation.org/73G75upA3).

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.330
Teacher spread0.307 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2018
Admission routes1
Has abstractyes

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