Radial head subluxation, also known as pulled elbow or nursemaid's elbow, is a common injury in young children. Supination and pronation techniques, used to reduce the elbow, are not commonly taught outside clinical settings. The novel trainer, the Michigan Elbow, was designed to support learning of reduction techniques. Prior to implementing, evaluating the models’ value across different specialties, where best practices and preferred reduction methods vary, is critical.
Two models, supination and pronation, included a 3D-printed plastic mechanism embedded in a silicon-based child-size arm form. A mechanism provided an audible click and haptic feedback when the elbow is reduced. Thirteen experienced pediatricians (P, n=13) and pediatric emergency physicians (PEM, n=8) independently evaluated both simulators’ physical attributes, realism, value, and relevance using a 14-item paper survey consisting of 4-point rating scales (4=highest). Participants’ reduction ability were self-reported using 5-point rating scales (5=very easy). Rating differences across specialties was tested using Kruskal-Wallis, with p≤ 0.05 being statistically significant and ɳ2≥0.5 considered moderate effect.
Pediatric and PEM participants self-reported an average of 15.2 and 43.5 lifetime elbow reductions, respectively. Pediatricians reported no preference to a reduction technique (50% split), while 7 (87.5%) of PEM participants preferred pronation. For most items, ratings were consistent and positive-leaning across specialties, with mean scores suggesting adequate physical attributes (MP= 3.35,SD=0.57 and MPEM=3.20,SD=.42, p>0.05), but low realism of experience (MP= 2.96,SD=0.53 and MPEM=2.73, SD=.89, p>0.05). PEM participants rated the simulator lower than pediatric participants for scale and feel of elbow when reduced, (p<0.05, ɳ 2≥0.14).Suggestions included size reduction and improving haptics on reduction of model.
Preliminary findings indicate the simulator holds promise for training after refinement. Difference in preferred reduction methods may have influenced ratings, but sample size was limiting. Future research targets model refinement and deeper examination of preferred techniques.