A real-time virtual outing using virtual reality for a hospitalized terminal cancer patient who has difficulty going out: a case report
Bibliographic record
Abstract
Objective: Even if hospitalized terminal cancer patients wish to go out, it is sometimes difficult for them to realize this because of various symptoms. We have been providing a virtual outing experience using virtual reality (VR) for terminal cancer patients who have difficulty going out, and have often received requests to “talk with people in the virtual outing,” but there is a problem that a large time lag occurs in conversation in a VR space under the general Internet environment. However, with the advent of systems that enable high-speed, low-latency communications, real-time communication is now possible even in VR spaces. Thus, we aimed to explore the feasibility of implementing the real-time virtual outing. Methods: The patient’s preferred virtual outing was to his daughter’s new home. The study operator used a 360° video real-time sharing system to broadcast the view of the daughter’s home. The patient experienced the images using a VR head-mounted display in his room. The patient’s wife, son, daughter, and grandson participated in this delivery using a laptop computer from a dayroom in the hospital, and his daughter’s husband participated using the 360° video real-time sharing system from the daughter’s home with the researcher. Before and after the virtual outing, changes in symptoms and emotions were assessed using the Edmonton Symptom Assessment System Revised Japanese version and the Numerical Rating Scale for headache, dizziness, pleasure, and satisfaction. In addition, we collected the patients’ impressions of the virtual outing. Results: The patient was a male in his early 70s. After approximately 30 min of real-time virtual outings, “tiredness, drowsiness, depression, and wellbeing” were improved and “pleasure, and satisfaction” were increased, while no side effects or worsening of symptoms were observed. In addition, it was observed from the patient’s comments that he felt a sense of presence, as if he were her home. Discussion: The patient and his family could enjoy smooth conversation without time lag even in the VR space. Therefore, it was suggested that real-time virtual outings using VR could help realize the wishes of hospitalized terminal cancer patients who have difficulty going out as a new approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".