Virtual reality as a strategy for intra-operatory anxiolysis and pharmacological sparing in patients undergoing breast surgeries: the V-RAPS randomized controlled trial protocol
Bibliographic record
Abstract
Abstract Introduction Virtual reality (VR) has increasingly found applications beyond leisure and video games, extending into the field of medicine. Recent studies indicate that VR can effectively reduce anxiety and pain in pediatric patients undergoing uncomfortable medical procedures, such as burn wound care. Yet, VR use in the operating room is still rare, despite a growing trend toward regional anesthesia without general anesthesia; physicians still frequently rely on pharmacological sedation to manage procedural anxiety. By leveraging VR’s anxiolytic properties, it may be possible to decrease the need for intravenous (IV) sedation which is associated with risk of adverse events like apnea and hypoxemia and delayed recovery. Objectives This study’s main objective is to explore the impact of VR on IV sedation requirements in adult patients undergoing breast surgery under paravertebral (PV) block without general anesthesia. We hypothesize that VR immersion will reduce the need for intraoperative pharmacological sedation. Secondary objectives include assessing the tolerance of patients to the VR headset, examining the impact of the chosen VR scenario on the primary outcome, evaluating the incidence of adverse effects, measuring patient satisfaction, and analyzing the output of the Nociception Level (NOL) Index among awake surgical patients. Material and methods This single center randomized controlled trial will enroll 100 patients aged 18 or above undergoing breast surgery under PV block. Participants will be randomly allocated to the VR group or the control group; both will have access to pharmacological sedation through patient-controlled sedation (PCS). Participants in the VR group will choose between three different VR scenarios and will be allowed to switch between these scenarios during surgery. The primary outcome will be the time-adjusted and weight adjusted dose of self-administered intraoperative propofol. Secondary outcomes will include patient satisfaction, adverse events, and post-anesthesia care unit length of stay (PACU LOS). Ethics This trial has been approved by the regional ethics committee (Comité d’Éthique de la Recherche du CIUSSS de l’Est de l’Île de Montréal) on September 9 th , 2024. Trial registration ClinicalTrials.gov (July 25 th , 2024). Unique protocol ID: 2025-3802. Trial identification number: NCT06522711 .
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".