Surgical simulation training and assessment model for supercharged end-to-side AIN-to-ulnar motor nerve transfers
摘要
Supercharging end-to-side (SETS) nerve transfers are increasingly used to augment reinnervation in muscles distal to proximal nerve injury. The anterior interosseous nerve (AIN) to motor branch of the ulnar nerve (MUN) SETS transfer has been adopted for high ulnar nerve lesions, but standardised training and assessment for the technique remain underdeveloped. This pilot study explored the feasibility of a structured educational model and use of a procedure-specific grading matrix for teaching and assessing the AIN-to-MUN SETS transfer in a body donor setting.
MethodsA 2-day prospective educational intervention was delivered to novice participants, ranging from medical student to fellow level. The programme combined didactic teaching, video demonstration and body donor dissection. Participants completed pre- and post-dissection self-assessments, including confidence and perceived performance ratings. Faculty assessed technical performance post-operatively and from standardised still images using a structured grading matrix. Inter- and intra-rater concordance were analysed descriptively.
ResultsNine participants completed 10 SETS dissections (1 repeat). Median self-reported preparedness improved from “somewhat prepared” to “fairly prepared,” (p = 0.0625). Participants generally self-rated performance as “good,” whereas faculty assessment suggested overestimation. Assessor ratings found, 70% of dissections achieved “fair” or better, representing the minimum acceptable standard for completion. Self-rated and faculty-rated performance showed little association (Spearman’s rho = 0.08, p = 0.84). Exact inter-rater concordance was 40%, with moderate weighted agreement (kappa = 0.52).
ConclusionsThis pilot study suggests that structured body donor training may be feasible for introducing the AIN-to-MUN SETS transfer. However, performance remained variable, self-assessment poorly reflected faculty assessment and the grading matrix requires further refinement before validation or wider implementation. However, represents a useful model for teaching a new microsurgical technique. Level of Evidence: not gradable.