Virtual reality for pain reduction during intravenous injection in pediatrics: a systematic review and meta-analysis of controlled clinical trials
Bibliographic record
Abstract
BACKGROUND: Intravenous (IV) injections often cause pain, fear, and anxiety in pediatric patients. Virtual reality (VR) is a relatively new intervention that can be used to provide a distraction during or prepare patients for IV injections. PURPOSE: To date, no meta-analysis has examined the evidence regarding the effectiveness of VR at reducing pain in pediatric IV injections. METHODS: The PubMed, Web of Science, Scopus, and Cochrane Central Register of Controlled Trials databases were searched for articles published through August 7, 2022. The methodological quality of the studies was measured using the Delphi checklist. The chi-square test and the I2 statistic were used to assess heterogeneity across studies. A summary measure of the mean difference in pain scores between the VR and control groups was obtained using a random effects model. All statistical analyses were set at a significance level of 0.05 using Stata 14. RESULTS: Nine studies were included in this meta-analysis of VR interventions used during IV injections in pediatric patients. The difference in mean pain score between the intervention and control groups showed significant reductions in the VR group (mean difference, 0.47; 95% confidence interval, 0.3-0.65; I2=9.1%). No interstudy heterogeneity was observed. CONCLUSION: Our results suggest that VR effectively reduces pain associated with IV injections in pediatric patients. No interstudy heterogeneity was noted among the analyzed studies. The Delphi checklist was used to assess methodological quality.
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 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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".