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BACKGROUND: Glenohumeral degenerative joint disease may affect up to 20% of the population. There are several classification systems of this disease in the scientific literature. OBJECTIVE: The aim of this study is to determine the reliability and reproducibility of glenohumeral osteoarthritis classification systems. METHODS: We assessed glenohumeral plain radiographs performed in a University Hospital. These radiographs were graded into nine radiological classification systems by two observers on two evaluations. Patients who have performed CT/MRI scan were staged according to the Walch classification. The intra-observer and inter-observer reliability of the classification schemes were determined by using Cohen's weighted kappa coefficient. RESULTS: A total of 752 patients were included in the study (52.4% females and 47.6% males), mean aged 66.2 ± 16.3 years. The intra-observer and inter-observer reliabilities were 0.543 (observer 1), 0.630 (observer 2), and 0.760 (inter-observer) for the Weinstein grading system; 0.661, 0.706, and 0.761 for the Guyette grading system; 0.575, 0.679 and 0.704 for the Kellgren and Lawrence classification; 0.817, 0.816 e 0.871 for the Samilson and Prieto classification; 0.791, 0.811 and 0.847 for the Allain modification; 0.797, 0.842 and 0.860 for the Gerber modification; 0.773, 0.827 and 0.828 for the Buscayret modification; 0.584, 0.648 and 0.755 for the Hawkins and Angelo classification; 0.661, 0.749 and 0.764 for the Rosenberg classification. Intra-observer reliability for MRI was 0.757 (observer 1) and 0.675 (observer 2), while intra-observer reliability for CT was 0.811 (observer 1) and 0.653 (observer 2). Inter-observer reliabilities were 0.790 for MRI and 0.673 for CT. CONCLUSION: The classification systems according to Weinstein, Guyette, Hawkins and Angelo, Rosenberg and the modifications of the Samilson and Prieto classification according to Allain, Gerber and Buscayret showed a comparable reliability with the commonly used glenohumeral osteoarthritis grading systems, Samilson and Prieto and Kellgren and Lawrence. Thus, they are recommended for clinical and especially scientific purposes. [ABSTRACT FROM AUTHOR]

Background: The pre-operative three-dimensional (3D) assessment of acetabular bone defects may not be evaluated properly with conventional radiographic and computed tomography images. This paper reports mid-term clinical and radiographic outcomes of complex revision total hip arthroplasty (r-THA) based on a 3D life-sized printed model. Methods: Patients who underwent r-THA for septic or aseptic acetabular loosening with acetabular defects Paprosky types IIc, IIIa, and IIIb between 2019 and 2021 were included. The outcomes of the study were to determine clinical and radiographic assessment outcomes at the time of the last follow-up. Results: 25 patients with mean age of 62.9 ± 10.8 (18–83) years old were included. The mean Harris hip score improved from 34.8 ± 8.1 pre-operative to 81.6 ± 10.4 points (p < 0.001). The mean visual analog scale decreased from 6.7 ± 1.4 points pre-operative to 2.4 ± 1.0 points (p < 0.001). The mean limb length discrepancy improved from −2.0 ± 1.2 cm pre-operative to −0.6 ± 0.6 cm (p < 0.001). The mean vertical position of the center of rotation (COR) changed from 3.5 ± 1.7 cm pre-operative to 2.0 ± 0.7 cm (p < 0.05). The mean horizontal COR changed from 3.9 ± 1.5 cm pre-operative to 3.2 ± 0.5 cm (p < 0.05). The mean acetabular component abduction angle changed from 59.7° ± 29.6° pre-operative to 46° ± 3.9 (p < 0.05). Conclusions: A three-dimensional-printed model provides an effective connection between the pre-operative bone defects' evaluation and the intraoperative findings, enabling surgeons to select optimal surgical strategies. [ABSTRACT FROM AUTHOR]

Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients' hospital length of stay (LOS). This study aims to develop a machine learning prediction model that may aid in predicting LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients who underwent elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization was classified as "short LOS" if it was less than or equal to 6 days and "long LOS" if it was greater than 7 days. Clinical data from pre-operative laboratory analysis, vital parameters, and demographic characteristics of patients were screened. Final data were used to train a logistic regression model with the aim of predicting short or long LOS. Results: The final dataset was composed of 1517 patients (795 "long LOS", 722 "short LOS", p = 0.3196) with a total of 1541 hospital admissions (729 "long LOS", 812 "short LOS", p < 0.001). The complete model had a prediction efficacy of 78.99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice determination of which patients are suitable for a shorter LOS and which for a longer LOS, in which a cautious approach could be recommended. [ABSTRACT FROM AUTHOR]