Validation of a Cost-effective Cast Saw Simulation-based Educational Module to Improve Cast Removal Safety.

Background Inexperience in cast removal in the pediatric population can lead to a range of cast saw-related injuries. The purpose of this study is to validate a simple simulation-based wax model that is both reproducible and economical while providing a valuable tool that can be used to grade cast saw use performance in trainees. Methods Cylindrical wax models were used as an analog for a pediatric upper extremity. The wax models were casted in a proscribed reproducible fashion for consistency. Two groups, the first consisting of 15 experienced cast saw users and the second consisting of 15 inexperienced individuals, completed 4 sequential longitudinal cuts in the casted wax models. After removal of the cast material, marks left by the cast saw in the wax were counted and measured. Indentation length, maximum depth, and maximum width were measured on each wax model. The total length of the cast saw indentations per cast saw user was also calculated. Results For the inexperienced cast saw users, the average total length of the cast saw indentations was 526.56 mm, average maximum depth was 1.91 mm, and average maximum width was 3.24 mm. For experienced cast saw users, the average total length of the cast saw indentations was 156.57 mm with an average maximum depth of 1.06 mm and average maximum width of 2.19 mm. Receiver operating characteristic curves of the total number of errors, total error length, maximum error depth, and maximum error width show effective discrimination of experienced from inexperienced trainees. Conclusions This study provides valid evidence supporting a cost-effective, time-efficient, and easily reproducible educational simulation module that can objectively measure cast saw the performance in trainees. This model demonstrates construct validity and can distinguish novice from experienced cast saw users. It is sensitive enough to identify mistakes even in the most experienced cast saw users, creating a platform that can provide performance-based feedback to cast saw users of all experience levels. Level of evidence Level III-diagnostic test.