Orthopaedics has been left behind in the worldwide drive towards diversity and inclusion. In the UK, only 7% of orthopaedic consultants are female. There is growing evidence that diversity increases innovation as well as patient outcomes. This paper has reviewed the literature to identify some of the common issues affecting female surgeons in orthopaedics, and ways in which we can address them: there is a wealth of evidence documenting the differences in the journey of men and women towards a consultant role. We also look at lessons learned from research in the business sector and the military. The ‘Hidden Curriculum’ is out of date and needs to enter the 21st century: microaggressions in the workplace must be challenged; we need to consider more flexible training options and support trainees who wish to become pregnant; mentors, both male and female, are imperative to provide support for trainees. The world has changed, and we need to consider how we can improve diversity to stay relevant and effective.

Cite this article: Bone Jt Open 2021;2-10:893–899.

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.