Virtual Reality to Reduce Procedural Pain During IV Insertion in the Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the feasibility of using virtual reality (VR) for distraction during intravenous (IV) insertion in the pediatric emergency department (ED) and of conducting a full-scale randomized controlled trial. MATERIALS AND METHODS: Children aged 8 to 17 years old attending a tertiary care pediatric ED were randomized to interactive VR or an attention control (video on a tablet) for distraction during their IV insertion. Feasibility was determined by recruitment rates, acceptability of the intervention, response rates to outcome measures, and safety or technical problems. Satisfaction questionnaires and pain, fear, and distress scores were completed by the child, caregiver, nurse, and research assistant. Immersion in the intervention was rated by the child. Heart rate was measured. RESULTS: Children were recruited between February 2018 and May 2019. A total of 116 children were screened and 72.3% of eligible children were enrolled. Overall, 60 children were randomized to either VR (n=32) or attention control (n=28). Children, caregivers, and nurses were highly satisfied with both distraction methods. There were no significant safety, technical, or equipment issues. There was minimal disruption to clinical workflow in both groups due to study protocols. There was a clinically significant reduction in pain in the VR group. There was no significant difference in fear or distress. Children reported higher immersion in the VR environment. Heart rate increase from baseline was higher in the VR group. DISCUSSION: Our data support the feasibility of using VR for distraction during IV insertion and of conducting a full-scale randomized controlled trial. Identifying eligible patients and minimizing the number of outcome measures will be important considerations for future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".