Physiological response to a virtual reality simulation for preoperative stress inoculation
Bibliographic record
Abstract
This paper describes the development of a novel immersive virtual reality (VR) simulation designed to reduce preoperative state anxiety in patients undergoing breast cancer surgery. A custom interactive VR simulation allows participants to experience the setting of an operating room and the key preoperative stages, all the way through the administration of general anesthesia. Interactivity is provided through a self-avatar with which the various simulated medical personnel interact directly. We evaluate the capacity of the simulation to induce an emotional response as measured by the participants’ galvanic skin response (GSR). To our knowledge, this is the first fully interactive simulation of an oncology surgery induction procedure for stress inoculation, and the first preoperative VR study to measure emotional impact using GSR. Out of a larger trial, we analyzed 6 participants who had been randomized to the simulation group and for whom baseline and intra-simulation GSR data had been successfully acquired. Three-minute samples were compared for statistical difference with a 95% confidence interval on the mean. 5 out of 6 showed a statistically significant and visually noticeable increase in GSR, and participants reported a high sense of spatial presence. Early results are encouraging, showing that the described simulation can induce a physiological response consistent with the participants' subjective evaluation of presence. While this was a limited experiment, it provides a basis for a larger trial to be conducted in the future.
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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) | 0.000 | 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 | 0.001 |
| 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".