Aims.
Aims. The optimal management of
Aims. The rationale for exacting restoration of skeletal anatomy after unstable ankle fracture is to improve outcomes by reducing complications from malunion; however, current definitions of malunion lack confirmatory clinical evidence. Methods. Radiological (absolute radiological measurements aided by computer software) and clinical (clinical interpretation of radiographs) definitions of malunion were compared within the Ankle Injury Management (AIM) trial cohort, including people aged ≥ 60 years with an unstable ankle fracture. Linear regressions were used to explore the relationship between radiological malunion (RM) at six months and changes in function at three years. Function was assessed with the Olerud-Molander Ankle Score (OMAS), with a minimal clinically important difference set as six points, as per the AIM trial. Piecewise linear models were used to investigate new radiological thresholds which better explain symptom impact on ankle function. Results. Previously described measures of RM and surgeon opinion of clinically significant malunion (CSM) were shown to be related but with important differences. CSM was more strongly related to outcome (-13.9 points on the OMAS; 95% confidence interval (CI) -21.9 to -5.4) than RM (-5.5 points; 95% CI -9.8 to -1.2). Existing malunion thresholds for talar tilt and tibiofibular clear space were shown to be slightly conservative; new thresholds which better explain function were identified (talar tilt > 2.4°; tibiofibular clear space > 6 mm). Based on this new definition the presence of RM had an impact on function, which was statistically significant, but the clinical significance was uncertain (-9.1 points; 95% CI -13.8 to -4.4). In subsequent analysis, RM of a
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. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.Aims
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