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Record W4401206401 · doi:10.1213/ane.0000000000007051

The Effectiveness of Virtual and Augmented Reality in Surgical Pain Management: A Systematic Review of Randomized Controlled Trials

2024· review· en· W4401206401 on OpenAlexaff
Tal Levit, Patrick Grzela, Declan C.T. Lavoie, Li Wang, Aashna Agarwal, Rachel Couban, Harsha Shanthanna

Bibliographic record

VenueAnesthesia & Analgesia · 2024
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineRandomized controlled trialPain managementPhysical therapyVirtual realityMEDLINEMedical physicsIntensive care medicineSurgeryHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Satisfactory management of postoperative pain remains challenging. Nonpharmacological modalities such as virtual and augmented reality (VR/AR) offer potential benefits and are becoming increasingly popular. This systematic review evaluates the effectiveness and safety of VR/AR interventions on postoperative pain and recovery. METHODS: MEDLINE, Embase, CINAHL, Web of Science, and CENTRAL databases were searched from inception to July 27, 2023, for randomized controlled trials (RCTs), published in English, evaluating the use of VR/AR interventions for surgical pain relief. Study selection and data extraction were performed by pairs of reviewers independently and in duplicate, and potential risk of bias was determined using the Risk of Bias-version 2 (RoB 2) tool. Our outcomes included pain relief, reduction of anxiety, satisfaction, and adverse effects. Due to substantial heterogeneity, a narrative synthesis without meta-analysis was performed. RESULTS: We included 35 trials among 2257 citations, categorized as surgery (n = 12), minor procedures (n = 15), and postoperative physiotherapy (n = 8). Surgical group included various surgeries, with 11 using immersive VR predominantly in the postoperative period, and most reporting no differences in pain, but potential for reduced anxiety and sedation requirements. In the minor procedures group, most studies reported decreased pain and anxiety during the procedural performance. Two studies reported increased heart rate, while 2 others reported better hemodynamic stability. Home-based AR physiotherapy achieved (n = 6) similar pain and functional outcomes after knee replacement, with 1 large study (n = 306) reporting reduction of mean costs by $2745 for provision of 12 weeks physiotherapy. There were some concerns around potential bias for most studies, as the nature of interventions make it challenging to blind assessors and participants. No important adverse effects were noted using VR/AR technology. CONCLUSIONS: Evidence from RCTs indicates that the use of immersive VR during minor procedures may reduce procedural pain, decrease anxiety, and improve satisfaction. However, small studies, inconsistent effect, and variation in the application of interventions are important limitations. Evidence to support the application of AR/VR for major surgeries is limited and needs to be further investigated. Use of home-based physiotherapy with AR likely has economic advantages, and facilitates virtual care for appropriate patients who can access and use the technology safely.

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 imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.075
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.016
Bibliometrics0.0100.009
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.026
GPT teacher head0.355
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2024
Admission routes1
Has abstractyes

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