Effects of Virtual Reality in Patients Undergoing Dialysis
Bibliographic record
Abstract
Dialysis is often considered slow, repetitive, and with programmed intervals. Patients often perceive it as time taken from their lives with a sense of ineluctability and emptiness, engendering a negative emotional and cognitive perception of the world and one's place in it. Today, it is possible to improve the quality of life of patients during hemodialysis using virtual reality (VR). This creation of a true multisensory experience may absorb the patient's perceptions during hemodialysis, improving his/her quality of life. An Italian multicenter, longitudinal experimental study will be conducted with a randomized, pre-post test design, with balanced allocation 1:1, in parallel groups with a control group in the standard care of patients diagnosed with chronic renal failure who are, undergoing hemodialysis treatment. A sample of 186 patients calculated with sample size (power = 80%, β = 0.2, α = 0.05) will be randomized into an experimental group exposed to VR, and a control group in standard care. The 2 groups will be studied over a period of 1 month, with 12 applications of VR and with measurements of the following outcomes: anxiety, fatigue, pruritus, arterial pressure, heart rate, respiration rate, and duration of the session at each hemodialysis session. This is the first international experimental protocol that examines the application of VR in patients undergoing hemodialysis. If the results show statistically and clinically significant differences, the VR could be an additional holistic intervention, which is evidence based, linked to the humanization of chronic, repetitive interventions, complementary to and synergistic with standard of care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".