Virtual reality distraction decreases pain during daily dressing changes following haemorrhoid surgery
Bibliographic record
Abstract
Objective To investigate whether immersive virtual reality (VR) distraction could decrease pain during postoperative dressing changes. Methods This was a prospective, open-label randomized clinical trial that enrolled patients that had undergone haemorrhoidectomy. Patients were randomly assigned to one of two groups: a control group that received the standard pharmacological analgesic intervention during dressing change and a VR group that received VR distraction during dressing change plus standard pharmacological analgesic intervention. Pain scores and physiological measurements were collected before, during and after the first postoperative dressing change. Results A total of 182 patients were randomly assigned to the control and VR groups. The baseline characteristics of the VR and control groups were comparable. There was no significant difference in mean pain scores prior to and after the dressing change procedure between the two groups. The mean pain scores at the 5-, 10-, 15- and 20-min time-points during the first dressing change were significantly lower in the VR group compared with the control group. Heart rates and oxygen saturation were not significantly different between the two groups. Conclusion Immersive VR was effective as a pain distraction tool in combination with standard pharmacological analgesia during dressing change in patients that had undergone haemorrhoidectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".