ePoster
Talk Description
Institution: Department of Otolaryngology-Head and Neck Surgery, Ehime University Graduate School of Medicine - Ehime, Japan
Aims: Tension management, including counter-traction, is the top-priority fundamental technique in soft tissue surgery, particularly head and neck surgery. However, its cognitive structure remains unexplored and unsystematized. This study aimed to elucidate how expert surgeons cognitively structure tension management during soft tissue dissection, and to examine its consistency across three specialties: head and neck surgery, gynecology, and general surgery.
Methodology: Focus groups were conducted with board-certified expert surgeons from three specialties: head and neck surgery (n=7), gynecology (n=7), and general surgery (n=8). All had ≥500 lifetime surgical cases. Sessions were audio-recorded and transcribed verbatim. Data were analyzed using reflexive thematic analysis through iterative within-specialty coding and cross-case synthesis.
Results: Analysis of 520 statements revealed a three-layered cognitive structure consistent across all specialties. The WHY layer (Core Principles) comprised three elements: safety (visibility, safety margin, plane clarification), efficiency (facilitation of dissection, device maneuverability), and collaboration (shared situational awareness). The HOW layer (Execution Parameters) consisted of foundational parameters (anatomical properties, device/platform constraints) and active parameters (optimal calibration, dynamic adjustment). The WHAT layer (Adjustment Strategies) consisted of three operational variables: traction point, vector, and magnitude.
Conclusion: Tension management, the core fundamental technique in head and neck surgery, is driven by a shared WHY-HOW-WHAT cognitive structure shared across specialties. Current training emphasizes the WHAT layer (e.g., "pull harder"); explicitly teaching WHY first may enable adaptive expertise. This framework provides a foundation for structured surgical education and semantic annotation in AI-assisted surgical support.
Presenters
Authors
Authors
Dr Sohei Mitani - , Dr Shiori Aibara - , Dr Saseem Poudel - , Dr Kazuhisa Kitami - , Dr Takeshi Kimura - , Dr Kayo Sakamoto -