Aims. The aim of this study was to investigate surgeons’ reported change of treatment preference in response to the results and conclusion from a randomized contolled trial (RCT) and to study patterns of change between subspecialties and nationalities. Methods. Two questionnaires were developed through the Delphi process for this cross-sectional survey of surgical preference. The first questionnaire was sent out before the publication of a RCT and the second questionnaire was sent out after publication. The RCT investigated repair or non-repair of the pronator quadratus (PQ) muscle during volar locked plating of distal radial fractures (DRFs). Overall, 380 orthopaedic surgeons were invited to participate in the first questionnaire, of whom 115 replied. One hundred surgeons were invited to participate in the second questionnaire. The primary outcome was the proportion of surgeons for whom a treatment change was warranted, who then reported a change of treatment preference following the RCT. Secondary outcomes included the reasons for repair or non-repair, reasons for and against following the RCT results, and difference of preferred treatment of the PQ muscle between surgeons of different nationalities, qualifications, years of training, and number of procedures performed per year. Results. Of the 100 surgeons invited for the second questionnaire, 74 replied. For the primary outcome, six of 32 surgeons (19%), who usually repaired the PQ muscle and therefore a change of treatment preference was warranted, reported a change of treatment preference based on the RCT publication. Of the secondary outcomes, restoring anatomy was the most common response for repairing the PQ muscle. Conclusion. The majority of the orthopaedic surgeons, where a change of treatment preference was warranted based on the results and conclusion of a RCT, did not report willingness to change their treatment preference. Cite this article: Bone Joint Open 2020;1-9:549–555

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.