Robotic-Assisted Surgery: The Intersection of Emotional Detachment and Adoption
Bibliographic record
Abstract
Despite strong evidence demonstrating the technical efficacy and safety of robotic surgery, emotional and cognitive factors heighten perceptions of risk, weaken trust in robotic systems, and contribute to resistance. We aim at studying how emotional notions of embodiment shape surgeons’ interactions with robotic systems and how these influence risk perceptions and impact adoption. Our goal is to reveal the complex interplay between robotic interfaces, the cognitive demands of decision-making, particularly in high–pressure surgical environments, and provide practical approaches to enhance haptic simulations to address the loss of tactile sensitivity, promote real-time robotic practice, and strategies to advance transitions from traditional surgery to robotic systems. Ultimately, the study findings will contribute to research by considering emotional and cognitive considerations when designing, training, and deploying robotic systems. Practically, it will facilitate a more integrative approach to robotic technology adoption in healthcare. Keywords Robotic-assisted surgery, embodiment, emotional detachment, sociomateriality
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".