Assessing the impact of mixed reality-assisted informed consent: A study protocol
Bibliographic record
Abstract
Background: Informed consent is a crucial aspect of modern medicine, but it can be challenging due to the complexity of the information involved. Mixed reality (MR) has emerged as a promising technology to improve communication. However, there is a lack of comprehensive research on the impact of MR on medical informed consent. The proposed research protocol provides a solid foundation for conducting future investigations and developing MR-based protocols that can enhance patients' understanding and engagement in the decision-making process. Methods: This study will employ a randomized controlled trial design. Two arms will be defined: MR-assisted informed consent (MRaIC) as the experimental arm and conventional informed consent (CIC) as the control arm consent, with 52 patients in each group. The protocol includes the use of questionnaires to analyze the anxiety levels and the awareness of the procedure that the patient is going to perform to study the impact of MRaIC versus CIC before medical procedures. Results: The study will evaluate the impact of MR on patients' information comprehension, engagement during the process of obtaining informed consent, emotional reactions, and consent decisions. Ethical concerns will be addressed. Conclusion: This study protocol provides a comprehensive approach to investigate the impact of MR on medical informed consent. The findings may contribute to a better understanding of the effects of MR on information comprehension, engagement during the process of obtaining informed consent, psychological experience, consent decisions, and ethical considerations. The integration of MR technology has the potential to enhance surgical communication practices and improve the informed consent process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".