The Therapeutic Application of Augmented and Virtual reality Technology in the Emergency Department: a Review of studies Conducted in the World
Bibliographic record
Abstract
Introduction: Advances in the field of technology in the field of virtualization today have led to the creation of tools that can easily evoke the presence of the patient in a more attractive situation than the current situation or by adding information to the real world to deepen the understanding of reality. Make Virtual reality and augmented reality technology has seriously entered the field of medicine for two decades. The emergency department has also been able to use this tool with its special features; therefore, the current research tries to have a systematic review of the therapeutic use of augmented and virtual reality technology in the emergency department. Methodology: This research is based on a systematic review in which the quality of the articles was evaluated using the STROBE standard checklist. The primary studies carried out, through articles on the use of virtual reality and augmented reality therapy in the emergency department; in Persian language databases; SID, Iranmedex, Magiran and English language databases; Science Direct, PubMed, Scopus and Google Scholar search engines were conducted until the end of September 2022. Keywords of Emergency, Augmented Reality, Virtual Reality, Mixed Reality, Treatment, Cure, Therapy, Medical, EMS, MR, VR, and AR were considered as a combination of words. Findings: Among the 14 reviewed studies, the largest number of extracted studies with 4 articles were conducted in Canada; and mostly it was a randomized and controlled prospective intervention method. Most of the studies with 10 articles were in the field of providing emergency services to children and adolescents under 18 years of age, and the effective components of virtualization technology were: reducing pain, managing anxiety and distress, improving hemodynamic parameters, and controlling fear and anger. Among the reviewed articles, none of the studies used augmented reality or mixed reality tools. Conclusion: The results of the collected studies on the use of virtualization therapy show that so far this tool has had significant effects on controlling emotions and improving hemodynamic parameters. Analyzing the mechanism of the effect of virtual reality in different groups can help to establish a cause and effect relationship and the targeted use of these tools. It is also necessary to design these tools in such a way that their use does not prevent the normal procedures in the emergency department.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".