Using Immersive Virtual Reality Distraction to Reduce Fear and Anxiety before Surgery
Bibliographic record
Abstract
Presurgical anxiety is very common and is often treated with sedatives. Minimizing or avoiding sedation reduces the risk of sedation-related adverse events. Reducing sedation can increase early cognitive recovery and reduce time to discharge after surgery. The current case study is the first to explore the use of interactive eye-tracked VR as a nonpharmacologic anxiolytic customized for physically immobilized presurgery patients. Method: A 44-year-old female patient presenting for gallbladder surgery participated. Using a within-subject repeated measures design (treatment order randomized), the participant received no VR during one portion of her preoperative wait and interactive eye-tracked virtual reality during an equivalent portion of time in the presurgery room. After each condition (no VR vs. VR), the participant provided subjective 0-10 ratings and state-trait short form Y anxiety measures of the amount of anxiety and fear she experienced during that condition. Results: As predicted, compared to treatment as usual (no VR), the patient reported having 67% lower presurgical anxiety during VR. She also experienced "strong fear" (8 out of 10) during no VR vs. "no fear" (0 out of 10) during VR. She reported a strong sense of presence during VR and zero nausea. She liked VR, she had fun during VR, and she recommended VR to future patients during pre-op. Interactive VR distraction with eye tracking was an effective nonpharmacologic technique for reducing anticipatory fear and anxiety prior to surgery. The results add to existing evidence that supports the use of VR in perioperative settings. VR technology has recently become affordable and more user friendly, increasing the potential for widespread dissemination into medical practice. Although case studies are scientifically inconclusive by nature, they help identify new directions for future larger, carefully controlled studies. VR sedation is a promising non-drug fear and anxiety management technique meriting further investigation.
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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 | 0.001 |
| 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) | 0.002 | 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".