Aims. This Delphi study assessed the challenges of diagnosing soft-tissue knee injuries (STKIs) in acute settings among orthopaedic healthcare stakeholders. Methods. This modified e-Delphi study consisted of three rounds and involved 32 orthopaedic healthcare stakeholders, including physiotherapists, emergency nurse practitioners, sports medicine physicians, radiologists, orthopaedic registrars, and orthopaedic consultants. The perceived importance of diagnostic components relevant to STKIs included patient and external risk factors, clinical signs and symptoms, special clinical tests, and diagnostic imaging methods. Each round required scoring and ranking various items on a ten-point Likert scale. The items were refined as each round progressed. The study produced rankings of perceived importance across the various diagnostic components. Results. In Round 1, the study revealed widespread variability in stakeholder opinions on diagnostic components of STKIs. Round 2 identified patterns in the perceived importance of specific items within each diagnostic component. Round 3 produced rankings of perceived item importance within each diagnostic component. Noteworthy findings include the challenges associated with accurate and readily available diagnostic methods in acute care settings, the consistent acknowledgment of the importance of adopting a patient-centred approach to diagnosis, and the transition from divergent to convergent opinions between Rounds 2 and 3. Conclusion. This study highlights the potential for a paradigm shift in acute STKI diagnosis, where variability in the understanding of STKI diagnostic components may be addressed by establishing a uniform, evidence-based framework for evaluating these injuries. Cite this article: Bone Jt Open 2024;5(11):984–991

Aims

No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model.