Virtual Reality for advanced cancer patients assisted at home: A randomized controlled interventional study
Bibliographic record
Abstract
OBJECTIVE: Virtual Reality (VR) has been demonstrated to be an effective option for integrating psychological interventions in different therapeutic settings. This randomized controlled interventional study aims to assess the effects of VR, compared to tablet controlled intervention, on anxiety, depression, pain, and short-term psychophysical symptoms in advanced cancer patients assisted at home. METHODS: Participants were provided with a VR headset or a tablet (TAB) for 4 days. On the first and last day, anxiety and depression were measured by Hospital Anxiety and Depression Scale and pain by Brief Pain Inventory. Before and after each VR and tablet session, symptoms were collected by the Edmonton Symptom Assessment Scale (ESAS). RESULTS: Fifty-three patients (27 VR vs. 26 TAB) completed the study. Anxiety significantly decreased in the VR group after the 4-day intervention. The analysis of ESAS showed a significant improvement in pain (p = 0.013), tiredness (p < 0.001), and anxiety (p = 0.013) for TAB group, and a significant reduction in tiredness (p < 0.001) in the VR group. CONCLUSIONS: Technological and user-friendly tools, such as VR and tablets, might be integrated with traditional psychological interventions to improve anxiety and cancer-related short-term symptoms. Further studies are needed to better consolidate the possible beneficial effects of VR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".