The Potential of Personalized Virtual Reality in Palliative Care: A Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Virtual Reality can help alleviate symptoms in a non-palliative care population. Personalized therapy can further alleviate these symptoms. There is little evidence in a palliative care population. AIM: To understand the feasibility of repeated personalized virtual reality sessions in a palliative care population. DESIGN: A feasibility randomized control trial. Intervention: personalized virtual reality, Control: non-personalized virtual reality. All participants completed a 4-minute virtual reality session for 4 weeks. At each point, the Edmonton Symptom Assessment System-Revised (scored 0 = none up to 100 = worst) was completed pre- and post- each session. A time-series regression analysis was completed for the overall effect. SETTING/PARTICIPANTS: The research took place in one hospice. The main inclusion criteria was: (1) under the care of the hospice (2) advanced disease (3) over 18 years (4) physically able to use virtual reality set (5) capacity (6) proficient English. RESULTS: Twenty-six participants enrolled, of which 20 (77%) completed all sessions. At baseline, the intervention group had a mean pre- score of 26.3 (SD 15.1) which reduced to 11.5 (SD 12.6) after the first session. At the same time point, the control group had a mean pre- score of 37.9 (SD 21.6) which reduced to 25.5 (SD 17.4) post-session. The mean scores dropped following each session, however this was not significant (mean difference = -1.3, 95% CI: -6.4 to 3.7, p = 0.601). CONCLUSIONS: It is feasible to complete repeated virtual reality sessions within a palliative care population. Future research should explore the structure and effectiveness of virtual reality in a fully powered trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".