A Novel Palliative Care Approach Using Virtual Reality for Improving Various Symptoms of Terminal Cancer Patients: A Preliminary Prospective, Multicenter Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background: Some terminal cancer patients wish to “go to a memorable place” or “return home.” However, owing to various symptom burdens and physical dysfunction, these wishes are difficult for them to realize. Objective: The aim of the study is to verify whether simulated travel using virtual reality (VR travel) is efficacious in improving symptoms in terminal cancer patients. Design: This is a prospective, multicenter, single-arm study. Setting/Subjects: Twenty participants with terminal cancer were recruited from two palliative care wards; data were collected from November 2017 to April 2018. Measurements: The VR software Google Earth VR ® was used. The primary endpoint was the change in the Edmonton Symptom Assessment System scores for each symptom before and after VR travel. Results: The average age of the participants was 72.3 (standard deviation [SD] = 11.9) years. Significant improvements were observed for pain (2.35, SD = 2.25 vs. 1.15, SD = 2.03, p = 0.005), tiredness (2.90, SD = 2.71 vs. 1.35, SD = 1.90, p = 0.004), drowsiness (2.70, SD = 2.87 vs. 1.35, SD = 2.30, p = 0.012), shortness of breath (1.74, SD = 2.73 vs. 0.35, SD = 0.99, p = 0.022), depression (2.45, SD = 2.63 vs. 0.40, SD = 0.82, p = 0.001), anxiety (2.60, SD = 2.64 vs. 0.80, SD = 1.51, p < 0.001), and well-being (4.50, SD = 2.78 vs. 2.20, SD = 1.99, p < 0.001; pre- vs. post-VR travel score, respectively). No participants complained of serious side effects. Conclusions: This preliminary study suggests that VR travel can be efficacious and safe for terminal cancer patients for improving symptom burden.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it