There is no absolute method of evaluating healing of a fracture of the tibial shaft. In this study we sought to validate a new clinical method based on the systematic observation of gait, first by assessing the degree of agreement between three independent observers regarding the gait score for a given patient, and secondly by determining how such a score might predict healing of a fracture.

We used a method of evaluating gait to assess 33 patients (29 men and four women, with a mean age of 29 years (15 to 62)) who had sustained an isolated fracture of the tibial shaft and had been treated with a locked intramedullary nail. There were 15 closed and 18 open fractures (three Gustilo and Anderson grade I, seven grade II, seven grade IIIA and one grade IIIB). Assessment was carried out three and six months post-operatively using videos taken with a digital camera. Gait was graded on a scale ranging from 1 (extreme difficulty) to 4 (normal gait). Bivariate analysis included analysis of variance to determine whether the gait score statistically correlated with previously validated and standardised scores of clinical status and radiological evidence of union.

An association was found between the pattern of gait and all the other variables. Improvement in gait was associated with the absence of pain on weight-bearing, reduced tenderness over the fracture, a higher Radiographic Union Scale in Tibial Fractures score, and improved functional status, measured using the Brazilian version of the Short Musculoskeletal Function Assessment questionnaire (all p < 0.001). Although further study is needed, the analysis of gait in this way may prove to be a useful clinical tool.

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

Objectives

Whilst gait speed is variable between healthy and injured adults, the extent to which speed alone alters the 3D in vivo knee kinematics has not been fully described. The purpose of this prospective study was to understand better the spatiotemporal and 3D knee kinematic changes induced by slow compared with normal self-selected walking speeds within young healthy adults.