Effect of Virtual Reality-Based Interventions on Pain During Wound Care in Burn Patients; a Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Introduction: Burn patients undergo daily painful wound care procedures, including washing, debridement, and dressing. This systematic review and meta-analysis aimed to examine the effect of virtual reality (VR)-based interventions on pain during wound care in burn patients. Methods: A comprehensive systematic search was conducted on international electronic databases such as Scopus, PubMed, and Web of Science with keywords extracted from Medical Subject Headings such as "Virtual reality", "Virtual reality therapy", "Virtual reality exposure therapy", "Virtual reality immersion therapy", "Exergaming", "Active-video gaming", "Burns", "Wound healings", "Pain", and "Pain management" from the earliest to May 6, 2022. The Joanna Briggs Institute (JBI) critical appraisal checklist was used to assess the quality of randomized control trials and quasi-experimental studies. Results: :91.2%). Conclusion: It is suggested that health policymakers and managers equip burn wards with immersive VR devices to provide the basis for this intervention when caring for patients with burn wounds.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.007 | 0.006 |
| 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".