GOING BANANAS – A RELIABLE LOW FIDELITY MODEL FOR THE ASSESSMENT AND TRAINING OF SUTURING SKILLS david_o_regan@um.edu.my

366-06

GOING BANANAS – A RELIABLE LOW FIDELITY MODEL FOR THE ASSESSMENT AND TRAINING OF SUTURING SKILLS

6

 

 

 

 

Malaysia

Oral only

Competency-based training requires reliable models and objective technical skill assessments, ideally with haptic and visual feedback to ensure proper technique. We present a scoring system that measures core elements of surgical stitching, such as spatial accuracy and needle rotation, using a low-fidelity banana model.

Participants suture around a 2 cm hole in banana skin, completing twelve radial stitches. Three surgeons and two trainees reviewed ninety-four photos from a surgical trainees’ competition, evaluating each according to six criteria: mushing, suture count, radial and equidistant placement, and skids or tears. A cumulative score was calculated based on key needle handling principles.

The overall ICC score demonstrated excellent reliability (ICCavg = 0.970, 95% CI: 0.960–0.979, p < 0.001). Among the individual domains, mush (ICCavg = 0.974, 95% CI: 0.964–0.981, p < 0.001) and number of suture (ICCavg = 0.976, 95% CI: 0.967–0.983, p < 0.001) also showed excellent reliability. RADIAN (ICCavg = 0.810, 95% CI: 0.742–0.865, p < 0.001), EQUIDISTANT (ICCavg = 0.799, 95% CI: 0.726–0.857, p < 0.001), TEAR (ICCavg = 0.829, 95% CI: 0.767–0.879, p < 0.001), skid and tear (ICCavg = 0.848, 95% CI: 0.793–0.892, p < 0.001) demonstrated good reliability.

A low-fidelity model effectively assesses fundamental surgical skills. Our findings demonstrate high interclass correlation and reliability of suturing skills. The scoring system allows students and trainers to objectively monitor stitching ergonomics and track progress in real time.