Virtual reality for reduction of intraprocedural pharmacological sedation and analgesia in adult patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Pharmacological sedation and analgesia are used to alleviate discomfort during awake medical procedures but can cause adverse effects like apnea and hypoxemia, increasing the need for airway management and prolonging recovery. Virtual reality (VR) has emerged as a non-pharmacological intervention to reduce the need for procedural sedatives and analgesics. A systematic review and meta-analysis were conducted, assessing the impact of VR immersion on intraprocedural sedation and analgesia usage in adults (≥ 18 years). We searched MEDLINE (PubMed), Embase, Cochrane CENTRAL, and Web of Science from inception to August 1st, 2024. We included analytical studies utilizing VR immersion in the intervention arm, and reporting tailored dosages of intraprocedural sedatives (propofol, midazolam) and/or opioids. Statistical analyses used standardized mean differences (SMD), and heterogeneity was assessed with I 2 . Of 2714 identified papers, 11 (560 patients) were included. VR significantly reduced propofol usage (SMD = −1.70; 95% CI −3.10 to −0.31; P = 0.02; I 2 = 92%) and midazolam usage (SMD = −0.29; 95% CI −0.57 to 0.00; P = 0.05; I 2 = 0%). However, our analysis showed no reduction in opioid usage (SMD = −0.21; 95% CI −0.60 to 0.19; P = 0.31; I 2 = 74%) in the VR group. VR immersion effectively reduces the required dose of intraprocedural sedatives, but its impact on opioid consumption remains unclear, especially in the absence of regional or neuraxial anesthesia. Further research is needed to clarify these effects and optimize VR use in clinical practice. This review’s protocol was prospectively registered on PROSPERO (CRD42024569462).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".