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Record W4403679750 · doi:10.1093/pch/pxae067.031

32 A randomized controlled trial of virtual reality-based distraction for intravenous cannulation-related distress in children

2024· article· en· W4403679750 on OpenAlexaboutno aff
Summer Hudson, Manasi Rajagopal, Katie Gourlay, Lisa Hartling, Jennifer Stinson, Keon Ma, Ben Vandermeer, Kurt Schreiner, Samina Ali

Bibliographic record

VenuePaediatrics & Child Health · 2024
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDistractionDistressRandomized controlled trialVirtual realityAnesthesiaMedicinePsychologySurgeryClinical psychologyComputer scienceCognitive psychologyHuman–computer interaction

Abstract

fetched live from OpenAlex

Abstract Background Children commonly experience under-treated pain and distress related to medical procedures, such as intravenous insertions (IVI), leading to both short- and long-term negative consequences for patients and their caregivers. Virtual reality (VR) has emerged as a promising tool to ameliorate procedure-related distress in the paediatric healthcare setting; however, existing evidence has largely employed custom VR software, which can be expensive and inaccessible. As such, we aimed to characterize whether commercially available VR applications are effective in reducing children’s procedure-related distress. Objectives To assess the effectiveness of commercially available VR-based distraction when added to standard of care (SOC, which included topical anesthetic cream), in reducing distress for children undergoing IVI in the paediatric emergency department (PED). Design/Methods Children aged 6 to 17 years requiring IVI in a Canadian PED were recruited for a two-arm randomized controlled trial. The primary outcome was child distress, measured using the Observational Scale of Behavioural Distress – Revised (OSBD-R, score range 0-23.5). Secondary outcomes included child pain intensity (measured with the Faces Pain Scale-Revised, score range 0-10) and caregiver anxiety [measured by the State-Trait Anxiety Inventory-State (STAI-S), score range 20-80] during the procedure. Trial registration number: NCT04291404C. Results 82 children and their caregivers were included. 41 children received VR distraction in addition to SOC (mean age 10.8 years, SD 3.0), while 41 children received SOC alone (mean age 11.3 years, SD 2.8). There was no significant difference between OSBD-R-measured distress in the VR arm (mean 1.05, SD 1.52) compared to the SOC arm (mean 0.71, SD 1.40) (p = 0.08). There was no significant reduction in procedural pain intensity in the VR group (mean 4.20,SD 3.10) compared to the SOC group (mean 4.10, SD 3.20) (p = 0.85). There was no difference in caregiver STAI-S score immediately following IVI in the VR arm (mean 32.3, SD 11.3) versus the SOC arm (mean 32.3, SD 11.9) (p = 0.86). Conclusion VR distraction therapy employing commercially available software was not associated with reduction in procedural distress or pain, above that provided with SOC in the PED, for children undergoing IVI. This result highlights the need for more accessible custom VR distraction software, created to meet the unique psychological needs of children undergoing medical procedures. Further study is needed to directly compare custom versus commercially available VR software for this application.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0160.001

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.

Opus teacher head0.010
GPT teacher head0.303
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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