Virtual reality for acute and chronic pain management in adult patients: a narrative review
Bibliographic record
Abstract
Virtual reality is a computer-generated environment that immerses the user in an interactive artificial world. This ability to distract from reality has been utilised for the purposes of providing pain relief from noxious stimuli. As technology rapidly matures, there is potential for anaesthetists and pain physicians to incorporate virtual reality devices as non-pharmacological therapy in a multimodal pain management strategy. This systematic narrative review evaluates clinical studies that used virtual reality in adult patients for management of acute and chronic pain. A literature search found 690 citations, out of which 18 studies satisfied the inclusion criteria. Studies were assessed for quality using the Jadad and Nottingham-Ottawa Scales. Agreement on scores between independent assessors was 0.87 (95%CI 0.73-0.94). Studies investigated virtual reality use: intra-operatively; for labour analgesia; for wound dressing changes; and in multiple chronic pain conditions. Twelve studies showed reduced pain scores in acute or chronic pain with virtual reality therapy, five studies showed no superiority to control treatment arms and in one study, the virtual reality exposure group had a worsening of acute pain scores. Studies were heterogeneous in: methods; patient population; and type of virtual reality used. These limitations suggest the evidence-base in adult patients is currently immature and more rigorous studies are required to validate the use of virtual reality as a non-pharmacological adjunct in multimodal pain management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".