Introduction. Lag screw cut-out following fixation of unstable intertrochanteric fractures in osteoporotic bone remains an unsolved challenge. A novel new device is the X-Bolt which is an expanding type bolt that may offer superior fixation in osteoporotic bone compared to the standard DHS screw type device. Aims. The aim of this study was to test if there was a difference in cut-out using the X-Bolt implant compared with the standard DHS system. Methods. Specimens of low density surrogate bone (5pcf) were inserted into a simplified biomechanical fracture model and had either an X-Bolt or DHS implant inserted. There were eight samples in each group. The fracture model was tested with an incremental cyclical loading programme in a Material Test System. Displacement, cycle count and force exerted were continuously recorded until cut-out of the implant. Results. All of the specimens failed by varus collapse with superior cut-out and resulted in an automatic stop of the MTS. Specimens with the X-Bolt implant inserted lasted longer on cyclical count and withstood a greater force at cut-out compared with DHS specimens. The mean number of cycles to cutout in the DHS specimens was 4345 and in specimens with the X-Bolt inserted was 6898. The mean force at which cutout occurred in the DHS group was 1.025kN and in specimens with the X-Bolt inserted was 1.275kN. A statistically significant difference was observed with a P-value of 0.005 and a power of 87.2% with respect to cycle count and a P-value of 0.008 and power 84.8% with respect to force exerted at failure when comparing between the two groups. Conclusion. This study shows that the X-Bolt device demonstrated superior cut-out resistance and withstood greater loads compared to the DHS in low density surrogate bone in an unstable fracture model under cyclical axial loading

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (https://www.ideal-collaboration.net/). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams.

Cite this article: Bone Joint Res 2024;13(9):507–512.