Polished taper-slip (PTS) cemented stems have an excellent clinical track record and are the most common stem type used in primary total hip arthroplasty (THA) in the UK. Due to low rates of aseptic loosening, they have largely replaced more traditional composite beam (CB) cemented stems. However, there is now emerging evidence from multiple joint registries that PTS stems are associated with higher rates of postoperative periprosthetic femoral fracture (PFF) compared to their CB stem counterparts. The risk of both intraoperative and postoperative PFF remains greater with uncemented stems compared to either of these cemented stem subtypes. PFF continues to be a devastating complication following primary THA and is associated with high complication and mortality rates. Recent efforts have focused on identifying implant-related risk factors for PFF in order to guide preventative strategies, and therefore the purpose of this article is to present the current evidence on the effect of cemented femoral stem design on the risk of PFF. Cite this article:
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
