Simulation Through Virtual Reality for the Management of Anxiety and Neuropathic Pain: Protocol for a Randomized Clinical Study
Bibliographic record
Abstract
Background: Neuropathic pain is a complex chronic pain condition often accompanied by affective symptoms such as anxiety and depression. This comorbidity is associated with increased pain severity, disability, and diminished quality of life, complicating treatment. Virtual reality (VR) is an innovative non-pharmacological intervention that is gaining attention for its potential to alleviate pain and anxiety through immersive distraction. The Protocol Committee of the University of Salamanca (Spain) has approved the study protocol (approval code: ID.1243). Objective: We aimed to evaluate the efficacy of VR in reducing pain and anxiety in patients with persistent neuropathic pain. Methods: This randomized, controlled, multicenter, open-label trial involves two groups: an intervention group receiving VR sessions and a control group receiving standard pharmacological treatment. Participants are adults aged 30-61 years diagnosed with neuropathic pain, unresponsive to flexible doses of gabapentin. Pain and anxiety levels are assessed using the Pain Detect Questionnaire, Visual Analog Scale (VAS), and Goldberg Anxiety Scale at baseline and follow-up points. VR sessions are conducted weekly for 3 weeks, each lasting 30-35 minutes. Perceived time during VR sessions is recorded as an indirect measure of distraction effectiveness. The primary outcome is the reduction in pain intensity and anxiety levels post-intervention compared to baseline. A sample size of 30 patients (15 per group) was calculated to achieve 80% statistical power, considering a 2-point mean difference in VAS scores. Results: A preliminary pilot study was completed with 16 patients, including 9 who received the virtual reality intervention. Initial findings showed reduced pain perception in 3 patients and decreased anxiety in 4 patients. These results informed the current protocol, which is scheduled to begin in 2025 and is currently in the preparatory phase. Conclusions: This study hypothesizes that VR will significantly reduce pain and anxiety in patients with persistent neuropathic pain. By providing new insights into non-pharmacological pain management strategies, this research aims to enhance the quality of life for these patients. The findings could support the broader application of VR in clinical 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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.009 |
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".