The Effects of Virtual Reality on Symptom Distress in Patients Undergoing Hematopoietic Stem Cell Transplant
Bibliographic record
Abstract
The purpose of this QI project was to evaluate the effects of virtual reality (VR) on symptom distress experienced by individuals receiving an allogenic stem cell transplant. Allogenic transplants are associated with a moderate to high risk for distressing symptoms such as depression, anxiety, and pain. VR targets multiple sensory modalities, including auditory, visual or haptic experiences, by using computer-generated scenarios, which can interact with an individual and possibly diminish unpleasant symptoms. Twenty individuals aged 19 to 70 years (median age of 56.5 years) who were hospitalized in an academic setting received VR up to two sessions a week for two weeks. Before and after each session, the participant completed the Edmonton Symptom Assessment Scale Revised (ESAS-r) to evaluate their symptom distress. Paired t-tests were conducted and showed significant improvement in eight out of the ten symptoms addressed in ESAS-r (depression, anxiety, tiredness, drowsiness, appetite, pain, quality of life, and wellbeing). Nausea and shortness of breath had no significant improvements. These findings suggest VR is a novel intervention to treat distressing symptoms in a hospital setting and warrant future investigations exploring VR’s impact on prolonged hospitalizations related to distressing symptoms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".