Aims Different methods of anterior cruciate ligament (ACL) reconstruction have been described for skeletally immature patients before closure of the growth plates. However, the outcome and complications following this treatment remain unclear. The aim of this systematic review was to analyse the outcome and complications of different techniques which may be used for reconstruction of the ACL in these patients. Materials and Methods We performed a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This involved a comprehensive search of PubMed, Medline, CINAHL, Cochrane, Embase and Google Scholar databases using the following combinations of keywords, “knee”, “anterior cruciate ligament”, “reconstruction”, “injury”, “children”, “adolescent”, “skeletally immature”, “open physis” and “surgery”. Results A total of 53 studies met the inclusion criteria and were included for analysis. The overall rate of disturbance of growth after ACL reconstruction was 2.6%, with no statistical difference between transphyseal and physeal-sparing techniques. Physeal-sparing techniques had a lower rate of post-operative complications compared with transphyseal techniques (p = 0.0045). Outcomes assessed were Lysholm score, International Knee Documentation Committee (IKDC) score, the IKDC grade, the Tegner score and the KT-1000. Both techniques had similar clinical outcomes. Conclusions This review reveals low rates of disturbance of growth after ACL reconstruction in skeletally immature patients. Although limited, the available evidence did not support any particular surgical technique when considering disturbance of growth or clinical outcome. Further randomised controlled trials are needed to investigate the efficacy of differing surgical techniques on outcomes in skeletally immature patients.

Background and Objectives: Unicompartmental knee arthroplasty (UKA) is a valid alternative to total knee arthroplasties (TKAs) in selected cases. After surgery, patients’ experience and satisfaction were traditionally evaluated by pre- and postsurgical scores and Patient-Reported Outcome Measures (PROMs). Otherwise, a statistically significant change does not necessarily correlate to a clinically meaningful improvement when measured using PROMs. To evaluate the real effect of a specific treatment and understand the difference between groups in a clinical trial, it is necessary to use a meaningful quantum of change on the score assessed. The minimal clinically important difference (MCID) and the Patient Acceptable Symptom State (PASS) can provide this meaningful change. This paper aimed to calculate the MCID and the PASS of the Forgotten Joint Score (FJS-12) after UKA. Materials and Methods: A total of 40 patients with a mean age 72.5 ± 6.4 years undergoing UKA were assessed preoperatively and six months postsurgery using the FJS-12 and the Oxford Knee Score (OKS). The baseline and 6-month postoperative scores were compared using the Wilcoxon signed ranks test. The correlation was calculated with Spearman’s rho. Both distribution-based approaches and anchor approaches were used to estimate MCID for the FJS-12. The 75th percentile and the Receiver operating characteristic (ROC) curve methods were used to calculate the PASS of FJS-12. Results: MCID estimates for normalized FJS-12 for UKA ranged from 5.68 to 19.82. The threshold of the FJS-12 with ROC method was 72.92 (AUC = 0.76). The cut-off value computed with the 75th percentile approach was 92.71. Conclusions: The MCID and PASS represent valid tools to assess the real perception of clinical improvement in patients who underwent UKA. The MCID value of FJS-12 was 12.5 for patients who underwent UKA. The value of the PASS for the FJS-12 in patients who underwent UKA was 72.92.

Introduction: This review aims to provide information on outcomes of surgical procedures for soft tissue or bony glenoid and/or humeral abnormalities in case of posterior shoulder instability. Source of data: A systematic review of the literature according to the PRISMA guidelines was performed. A comprehensive search of PubMed, Medline, CINAHL, Cochrane, Embase, Ovid and Google Scholar databases using various combinations of the keywords ‘shoulder’, ‘posterior instability’, ‘dislocation’, ‘reversed bony bankart’, ‘reversed Hill Sachs’, and ‘capsulolabral’ was performed. Areas of agreement: A total of 847 shoulders in 810 patients were included. A redislocation event occurred in 33 (8.7%) of 411 shoulders with soft tissue abnormalities and in 12 (9.1%) of 132 shoulders with bony abnormalities of the glenoid, humeral head or both. Areas of controversy: The optimal treatment modalities for posterior shoulder dislocation remain to be defined. Growing points: Operative stabilization for posterior shoulder instability should be lesion-specific and should correct all components of the posterior instability. Areas timely for developing research: Future prospective studies should aim to establish the optimal treatment modalities for posterior shoulder instability.

Background: Socially Assistive Robots (SARs) represent an innovative approach in rehabilitation technology, significantly enhancing the support and motivation for individuals across diverse rehabilitation settings. Despite their growing utilization, especially in stroke recovery and pediatric rehabilitation, their potential in musculoskeletal and orthopedic rehabilitation remains largely underexplored. Although there is methodological and outcome variability across the included studies, this review aims to critically evaluate and summarize the research on SARs in rehabilitation, providing a thorough overview of the current evidence and practical applications. Methods: A comprehensive search was conducted across multiple databases, resulting in the selection of 20 studies for analysis. The reviewed papers were categorized into three main classes based on the roles of the robots in rehabilitation: Motivation, Imitation, and Feedback Providers. Results: The analysis highlights that SARs significantly improve adherence to rehabilitation programs, enhance motor function, and increase motivation across clinical and home settings. Robots such as NAO, Pepper, and ZORA demonstrated high efficacy, particularly in stroke recovery and pediatric rehabilitation. Conclusions: SARs offer transformative benefits in rehabilitation, providing scalable, personalized solutions through motivational support, guided exercises, and real-time feedback. Their integration into orthopedic rehabilitation could address critical clinical needs, enhancing precision in exercises, adherence to long-term programs, and overall patient outcomes. Future research should prioritize the development and validation of SAR-based interventions for musculoskeletal disorders to unlock their full potential in this domain.

Background and Objectives: Knee osteoarthritis is a serious burden for modern countries. Timing of surgery and treatment choice are still a matter of controversy in the orthopedic literature. The purpose of this study was to ascertain the incidence and hospitalization trends of high tibial osteotomy in Italy from 2001 to 2016. Materials and Methods: Data are sourced from the National Hospital Discharge Reports (SDO) of the Italian Ministry of Health between 2001 and 2016. Results: A total of 34,402 high tibial osteotomies were performed over the study period in Italy. The cumulative incidence was 3.6 cases per 100,000 residents. The age classes 50–54, 55–59 showed the higher number of procedures. In pediatric patients (0–19 years), high tibial osteotomies are also largely performed. The majority of patients having surgery were men with a M/F ratio of 1.5. The mean age of patients was 44.2 ± 19.2 years. Males were significantly younger than females (43.3 ± 20.7 vs. 45.6 ± 17.7). The average length of hospitalization was 6.1 ± 7.3 days. Over the course of the analysis, a declining trend in hospital stay length was seen. The main primary diagnosis codes were “Varus knee” (736.42 ICD-9-CM code, 33.9%), “Osteoarthrosis, localized, primary, leg region” (715.16 ICD-9-CM code, 9.5%). Conclusions: Over the study period, high tibial osteotomies in Italy almost halved. Varus deformity and knee osteoarthritis are the leading causes requiring high tibial osteotomy. Except for the pediatric setting, results showed that from the 20–24 age class to the 50–54 age class, there was an increasing request for knee osteotomy, whereas in those aged >60 years, the incidence progressively decreased. The evident decline in HTO performed over the years in Italy seems to reflect a minor role for knee osteotomy in the management of knee OA, as it seems to be primarily reserved for younger male patients.

Aim: The overall aim of this proposal is to ameliorate the care of rotator cuff (RC) tear patients by applying an innovative machine learning approach for outcome prediction after arthroscopic repair. Materials and Methods: We applied state-of-the-art machine learning algorithms to evaluate the best predictors of the outcome, and 100 RC patients were evaluated at baseline (T0), after 1 month (T1), 3 months (T2), 6 months (T3), and 1 year (T4) from surgical intervention. The outcome measure was the Costant–Murley Shoulder Score, whereas age, sex, BMI, the 36-Item Short-Form Survey, the Simple Shoulder Test, the Hospital Anxiety and Depression Scale, the American Shoulder and Elbow Surgeons Score, the Oxford Shoulder Score, and the Shoulder Pain and Disability Index were considered as predictive factors. Support vector machine (SVM), k-nearest neighbors (k-NN), naïve Bayes (NB), and random forest (RF) algorithms were employed. Results: Across all sessions, the classifiers demonstrated suboptimal performance when using both the complete and shrunken sets of features. Specifically, the logistic regression (LR) classifier achieved a mean accuracy of 46.5% ± 6%, while the random forest (RF) classifier achieved 51.25% ± 4%. For the shrunken set of features, LR obtained a mean accuracy of 48.5% ± 6%, and RF achieved 45.5% ± 4.5%. No statistical differences were found when comparing the performance metrics of ML algorithms. Conclusions: This study underlines the importance of extending the application of AI methods to new predictors, such as neuroimaging and kinematic data, in order to better record significant shifts in RC patients’ prognosis. Limitations: The data quality within the cohort could represent a limitation, since certain variables, such as smoking, diabetes, and work injury, are known to have an impact on the outcome.