Age and Sex Differences of Virtual Reality Pain Alleviation Therapeutic During Pediatric Burn Care: A Randomized Clinical Trial
Bibliographic record
Abstract
Virtual reality (VR) effectively alleviates pain for pediatric patients during many medical procedures, such as venipuncture and burn care. In our previously published randomized clinical trial among 90 pediatric burn patients, participants in the active VR group had significantly lower scores for overall pain compared with participants in the standard care control and for worst pain compared with participants in the passive VR and control group. However, whether VR differs by a patient’s age or sex remains unresolved. Thus, we reanalyzed our data by comparing the active and passive VR participants to evaluate how age and sex affect VR pain alleviation during dressing care for pediatric burns. In total, 90 patients aged 6–17 years (inclusive) with burn injuries were recruited from an outpatient burn clinic of an American Burn Association-verified pediatric burn center. Before randomization, VR helpfulness and need expectations were assessed on a visual analog scale (0–100). Participants were randomly assigned to active VR, passive VR, or control for one dressing change. Immediately following the dressing change, active and passive VR participants self-reported pain and the time spent thinking about pain and rated the VR features on the degree of realism, pleasure/fun, and perceived engagement level. Path analyses assessed how these VR features were interrelated and how they affected self-reported pain by age and sex. Patients aged 6–9 years reported higher mean expectations of VR helpfulness and need (mean = 73.6 and 94.5, respectively) than 10–12-year-olds (mean = 55.7 and 84.2, respectively) and 13–17-year-olds (mean = 68.6 and 77.4, respectively). The path analysis indicated VR engagement and fun were significantly correlated (p-value < 0.05). VR engagement significantly negatively impacted overall pain scores (coefficient = −0.45, −0.41; p-value < 0.05) and significantly positively impacted time thinking of pain (coefficient = 0.38, 0.32; p-value < 0.05). Younger patients had the highest expectations of VR helpfulness and need. VR game realism, fun, and engagement features were not statistically different between age groups and sexes. VR engagement and thinking of pain during burn dressing significantly positively affected self-reported pain (p-value < 0.05), suggesting an analgesic mechanism beyond distraction alone. Younger patients benefited more from VR than older patients.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".