The effect of Virtual Reality (VR) on anxiety and pain in patients undergoing port implantation
Bibliographic record
Abstract
Abstract A growing body of evidence supports Virtual Reality (VR) as an effective and safe strategy for management of pain and stress associated with medical procedures in both adults and children. We therefore initiated a feasibility study to investigate the effect of VR on pain, stress, and anxiety during elective surgery, e.g. implantation of a central-venous port catheter, hypothesizing that VR can reduce intraoperative pain, stress and anxiety of the patient. In this manuscript, the preliminary results of the first 20 (out of 6o planned) patients are presented. Baseline pain characteristics did not differ between the two study groups (VR group (n=10) and standard (no VR device) group (n=10)). System usability (“easy to use”, “easy to learn” and “safe”) was rated “good - very good” by the study participants. Selfassessment of anxiety components (Y-6 item questionnaire) revealed a calming (3.3 ± 0.5 vs. 2.4 ± 0.5, P= 0.009) and relaxing (2.7 ± 1.2 vs. 1.8 ± 0.4; P=0.09) effect of the VR device. Evaluation of pain level (Short form McGill questionnaire) during the procedure revealed a lower pain intensity (VAS) level (17.5 ± 12.1 vs. 19.5 ± 10.6; P= 0.834) and present pain intensity (PPI) score (0.9 ± 0.6 vs. 1.0 ± 0.5; P= 0.841) in the VR group Preliminary data of our feasibility study indicates a positive effect of VR towards reduction of pain and stress in patients undergoing minor surgery in local anaesthesia. However, further data is needed to substantiate these results.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".