Virtual reality hypnosis prior to radiofrequency thermocoagulation for patients with chronic pain: an exploratory clinical trial
Bibliographic record
Abstract
Background: The management of chronic pain may involve an array of tools, including radiofrequency thermocoagulation (Rf-Tc) of sensory nerve terminals. Like many other invasive procedures, Rf-Tc can generate anxiety in a lot of patients, either during the expectation of the procedure or in the course of it. Virtual reality hypnosis (VRH) is a promising tool for managing anxiety and pain in several situations, but its anxiolytic property has not been investigated in participants with chronic pain and going through a Rf-Tc procedure. Objectives: The goal of this study was to evaluate the effectiveness of VRH for reducing self-assessed anxiety in participants with chronic pain, when received in preparation for Rf-Tc. Materials and methods: This prospective, controlled trial was conducted in the Interdisciplinary Algology Centre of the University Hospital of Liège (Belgium). Participants were assigned to two groups: VRH or control (usual care). Assessment was carried-out at 4 time points: T0 (one week before Rf-Tc); T1 (pre-intervention, on the day of Rf-Tc); T2 (immediately after the VRH intervention outside of the Rf-Tc room); and T3 (right after Rf-Tc). Medical, sociodemographic data, anxiety trait and immersive tendencies were collected at T0. Anxiety state and pain intensity were assessed at each time points. Satisfaction was examined at T3. Results: Forty-two participants were quasi-randomly assigned to the VRH or control group. No statistically significant interaction group by time was observed regarding all measured variables, including primary endpoint. However, a significant effect of time was found for anxiety and pain when considering both groups together, toward a progressive reduction. Conclusion: In the context of our study, there appears to be no significant effect of VRH at reducing anxiety in participants with chronic pain undergoing Rf-Tc. Anxiety decreases along the procedure, while pain is attenuated by the local anesthetic infiltration of the Rf site. Our results suggest that the presence of a caregiver throughout the procedure might explain the progressive decrease in anxiety. Future randomized controlled trials are needed to precisely study the effectiveness of the VRH tool, and the possibility of using it as a complementary approach for anxiety during invasive procedures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".