Aims. To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. Methods. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). Results. The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Conclusion. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making. Cite this article: Bone Jt Open 2023;4(3):168–181

Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

Aims. There remains a lack of consensus regarding the management of chronic anterior sternoclavicular joint (SCJ) instability. This study aimed to assess whether a standardized treatment algorithm (incorporating physiotherapy and surgery and based on the presence of trauma) could successfully guide management and reduce the number needing surgery. Methods. Patients with chronic anterior SCJ instability managed between April 2007 and April 2019 with a standardized treatment algorithm were divided into non-traumatic (offered physiotherapy) and traumatic (offered surgery) groups and evaluated at discharge. Subsequently, midterm outcomes were assessed via a postal questionnaire with a subjective SCJ stability score, Oxford Shoulder Instability Score (OSIS, adapted for the SCJ), and pain visual analogue scale (VAS), with analysis on an intention-to-treat basis. Results. A total of 47 patients (50 SCJs, three bilateral) responded for 75% return rate. Of these, 31 SCJs were treated with physiotherapy and 19 with surgery. Overall, 96% (48/50) achieved a stable SCJ, with 60% (30/50) achieving unrestricted function. In terms of outcomes, 82% (41/50) recorded good-to-excellent OSIS scores (84% (26/31) physiotherapy, 79% (15/19) surgery), and 76% (38/50) reported low pain VAS scores at final follow-up. Complications of the total surgical cohort included a 19% (5/27) revision rate, 11% (3/27) frozen shoulder, and 4% (1/27) scar sensitivity. Conclusion. This is the largest midterm series reporting chronic anterior SCJ instability outcomes when managed according to a standardized treatment algorithm that emphasizes the importance of appropriate patient selection for either physiotherapy or surgery, based on a history of trauma. All but two patients achieved a stable SCJ, with stability maintained at a median of 70 months (11 to 116) for the physiotherapy group and 87 months (6 to 144) for the surgery group. Cite this article: Bone Jt Open 2022;3(10):815–825

Aims. The management of mid-shaft clavicle fractures (MSCFs) has evolved over the last three decades. Controversy exists over which specific fracture patterns to treat and when. This review aims to synthesize the literature in order to formulate an appropriate management algorithm for these injuries in both adolescents and adults. Methods. This is a systematic review of clinical studies comparing the outcomes of operative and nonoperative treatments for MSCFs in the past 15 years. The literature was searched using, PubMed, Google scholar, OVID Medline, and Embase. All databases were searched with identical search terms: mid-shaft clavicle fractures (± fixation) (± nonoperative). Results. Using the search criteria identified, 247 studies were deemed eligible. Following initial screening, 220 studies were excluded on the basis that they were duplicates and/or irrelevant to the research question being posed. A total of 27 full-text articles remained and were included in the final review. The majority of the meta-analyses draw the same conclusions, which are that operatively treated fractures have lower nonunion and malunion rates but that, in those fractures which unite (either operative or nonoperative), the functional outcomes are the same at six months. Conclusion. With regard to the adolescent population, the existing body of evidence is insufficient to support the use of routine operative management. Regarding adult fractures, the key to identifying patients who benefit from operative management lies in the identification of risk factors for nonunion. We present an algorithm that can be used to guide both the patient and the surgeon in a joint decision-making process, in order to optimize patient satisfaction and outcomes. Cite this article: Bone Jt Open 2022;3(11):850–858

Aims. Once knee arthritis and deformity have occurred, it is currently not known how to determine a patient’s constitutional (pre-arthritic) limb alignment. The purpose of this study was to describe and validate the arithmetic hip-knee-ankle (aHKA) algorithm as a straightforward method for preoperative planning and intraoperative restoration of the constitutional limb alignment in total knee arthroplasty (TKA). Methods. A comparative cross-sectional, radiological study was undertaken of 500 normal knees and 500 arthritic knees undergoing TKA. By definition, the aHKA algorithm subtracts the lateral distal femoral angle (LDFA) from the medial proximal tibial angle (MPTA). The mechanical HKA (mHKA) of the normal group was compared to the mHKA of the arthritic group to examine the difference, specifically related to deformity in the latter. The mHKA and aHKA were then compared in the normal group to assess for differences related to joint line convergence. Lastly, the aHKA of both the normal and arthritic groups were compared to test the hypothesis that the aHKA can estimate the constitutional alignment of the limb by sharing a similar centrality and distribution with the normal population. Results. There was a significant difference in means and distributions of the mHKA of the normal group compared to the arthritic group (mean -1.33° (SD 2.34°) vs mean -2.88° (SD 7.39°) respectively; p < 0.001). However, there was no significant difference between normal and arthritic groups using the aHKA (mean -0.87° (SD 2.54°) vs mean -0.77° (SD 2.84°) respectively; p = 0.550). There was no significant difference in the MPTA and LDFA between the normal and arthritic groups. Conclusion. The arithmetic HKA effectively estimated the constitutional alignment of the lower limb after the onset of arthritis in this cross-sectional population-based analysis. This finding is of significant importance to surgeons aiming to restore the constitutional alignment of the lower limb during TKA. Cite this article: Bone Jt Open 2021;2(5):351–358

Aims. Total hip arthroplasty (THA) in patients with post-polio residual paralysis (PPRP) is challenging. Despite relief in pain after THA, pre-existing muscle imbalance and altered gait may cause persistence of difficulty in walking. The associated soft tissue contractures not only imbalances the pelvis, but also poses the risk of dislocation, accelerated polyethylene liner wear, and early loosening. Methods. In all, ten hips in ten patients with PPRP with fixed pelvic obliquity who underwent THA as per an algorithmic approach in two centres from January 2014 to March 2018 were followed-up for a minimum of two years (2 to 6). All patients required one or more additional soft tissue procedures in a pre-determined sequence to correct the pelvic obliquity. All were invited for the latest clinical and radiological assessment. Results. The mean Harris Hip Score at the latest follow-up was 79.2 (68 to 90). There was significant improvement in the coronal pelvic obliquity from 16.6. o. (SD 7.9. o. ) to 1.8. o. (SD 2.4. o. ; p < 0.001). Radiographs of all ten hips showed stable prostheses with no signs of loosening or migration, regardless of whether paralytic or non-paralytic hip was replaced. No complications, including dislocation or infection related to the surgery, were observed in any patient. The subtrochanteric shortening osteotomy done in two patients had united by nine months. Conclusion. Simultaneous correction of soft tissue contractures is necessary for obtaining a stable hip with balanced pelvis while treating hip arthritis by THA in patients with PPRP and fixed pelvic obliquity. Cite this article: Bone Jt Open 2021;2(9):696–704

Aims. Fungal periprosthetic joint infections (fPJIs) are rare complications, constituting only 1% of all PJIs. Neither a uniform definition for fPJI has been established, nor a standardized treatment regimen. Compared to bacterial PJI, there is little evidence for fPJI in the literature with divergent results. Hence, we implemented a novel treatment algorithm based on three-stage revision arthroplasty, with local and systemic antifungal therapy to optimize treatment for fPJI. Methods. From 2015 to 2018, a total of 18 patients with fPJI were included in a prospective, single-centre study (DKRS-ID 00020409). The diagnosis of PJI is based on the European Bone and Joint Infection Society definition of periprosthetic joint infections. The baseline parameters (age, sex, and BMI) and additional data (previous surgeries, pathogen spectrum, and Charlson Comorbidity Index) were recorded. A therapy protocol with three-stage revision, including a scheduled spacer exchange, was implemented. Systemic antifungal medication was administered throughout the entire treatment period and continued for six months after reimplantation. A minimum follow-up of 24 months was defined. Results. Eradication of infection was achieved in 16 out of 18 patients (88.8%), with a mean follow-up of 35 months (25 to 54). Mixed bacterial and fungal infections were present in seven cases (39%). The interval period, defined as the period of time from explantation to reimplantation, was 119 days (55 to 202). In five patients, a salvage procedure was performed (three cementless modular knee arthrodesis, and two Girdlestone procedures). Conclusion. Therapy for fPJI is complex, with low cure rates according to the literature. No uniform treatment recommendations presently exist for fPJI. Three-stage revision arthroplasty with prolonged systemic antifungal therapy showed promising results. Cite this article: Bone Jt Open 2021;2(8):671–678

Aims. The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. Methods. We developed CAT algorithms for the OHS, OKS, OSS, overall OES, and each of the OES subscales, using responses to the full-length questionnaires and a machine-learning technique called regression tree learning. The algorithms were evaluated through a series of simulation studies, in which they aimed to predict respondents’ full-length questionnaire scores from only a selection of their item responses. In each case, the total number of items used by the CAT algorithm was recorded and CAT scores were compared to full-length questionnaire scores by mean, SD, score distribution plots, Pearson’s correlation coefficient, intraclass correlation (ICC), and the Bland-Altman method. Differences between CAT scores and full-length questionnaire scores were contextualized through comparison to the instruments’ minimal clinically important difference (MCID). Results. The CAT algorithms accurately estimated 12-item questionnaire scores from between four and nine items. Scores followed a very similar distribution between CAT and full-length assessments, with the mean score difference ranging from 0.03 to 0.26 out of 48 points. Pearson’s correlation coefficient and ICC were 0.98 for each 12-item scale and 0.95 or higher for the OES subscales. In over 95% of cases, a patient’s CAT score was within five points of the full-length questionnaire score for each 12-item questionnaire. Conclusion. Oxford Hip Score, Oxford Knee Score, Oxford Shoulder Score, and Oxford Elbow Score (including separate subscale scores) CATs all markedly reduce the burden of items to be completed without sacrificing score accuracy. Cite this article: Bone Jt Open 2022;3(10):786–794

Aims. Our objective was describing an algorithm to identify and prevent vascular injury in patients with intrapelvic components. Methods. Patients were defined as at risk to vascular injuries when components or cement migrated 5 mm or more beyond the ilioischial line in any of the pelvic incidences (anteroposterior and Judet view). In those patients, a serial investigation was initiated by a CT angiography, followed by a vascular surgeon evaluation. The investigation proceeded if necessary. The main goal was to assure a safe tissue plane between the hardware and the vessels. Results. In ten at-risk patients undergoing revision hip arthroplasty and submitted to our algorithm, six were recognized as being high risk to vascular injury during surgery. In those six high-risk patients, a preventive preoperative stent was implanted before the orthopaedic procedure. Four patients needed a second reinforcing stent to protect and to maintain the vessel anatomy deformed by the intrapelvic implants. Conclusion. The evaluation algorithm was useful to avoid blood vessels injury during revision total hip arthroplasty in high-risk patients. Cite this article: Bone Jt Open 2022;3(11):859–866

Aims. Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results. The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion. We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients. Cite this article: Bone Jt Open 2024;5(2):101–108

Aims. An algorithm to determine the constitutional alignment of the lower limb once arthritic deformity has occurred would be of value when undertaking kinematically aligned total knee arthroplasty (TKA). The purpose of this study was to determine if the arithmetic hip-knee-ankle angle (aHKA) algorithm could estimate the constitutional alignment of the lower limb following development of significant arthritis. Methods. A matched-pairs radiological study was undertaken comparing the aHKA of an osteoarthritic knee (aHKA-OA) with the mechanical HKA of the contralateral normal knee (mHKA-N). Patients with Grade 3 or 4 Kellgren-Lawrence tibiofemoral osteoarthritis in an arthritic knee undergoing TKA and Grade 0 or 1 osteoarthritis in the contralateral normal knee were included. The aHKA algorithm subtracts the lateral distal femoral angle (LDFA) from the medial proximal tibial angle (MPTA) measured on standing long leg radiographs. The primary outcome was the mean of the paired differences in the aHKA-OA and mHKA-N. Secondary outcomes included comparison of sex-based differences and capacity of the aHKA to determine the constitutional alignment based on degree of deformity. Results. A total of 51 radiographs met the inclusion criteria. There was no significant difference between aHKA-OA and mHKA-N, with a mean angular difference of −0.4° (95% SE −0.8° to 0.1°; p = 0.16). There was no significant sex-based difference when comparing aHKA-OA and mHKA-N (mean difference 0.8°; p = 0.11). Knees with deformities of more than 8° had a greater mean difference between aHKA-OA and mHKA-N (1.3°) than those with lesser deformities (-0.1°; p = 0.009). Conclusion. This study supports the arithmetic HKA algorithm for prediction of the constitutional alignment once arthritis has developed. The algorithm has similar accuracy between sexes and greater accuracy with lesser degrees of deformity. Cite this article: Bone Joint Open 2020;1-7:339–345

Aims. Custom-made partial pelvis replacements (PPRs) are increasingly used in the reconstruction of large acetabular defects and have mainly been designed using a triflange approach, requiring extensive soft-tissue dissection. The monoflange design, where primary intramedullary fixation within the ilium combined with a monoflange for rotational stability, was anticipated to overcome this obstacle. The aim of this study was to evaluate the design with regard to functional outcome, complications, and acetabular reconstruction. Methods. Between 2014 and 2023, 79 patients with a mean follow-up of 33 months (SD 22; 9 to 103) were included. Functional outcome was measured using the Harris Hip Score and EuroQol five-dimension questionnaire (EQ-5D). PPR revisions were defined as an endpoint, and subgroups were analyzed to determine risk factors. Results. Implantation was possible in all cases with a 2D centre of rotation deviation of 10 mm (SD 5.8; 1 to 29). PPR revision was necessary in eight (10%) patients. HHS increased significantly from 33 to 72 postoperatively, with a mean increase of 39 points (p < 0.001). Postoperative EQ-5D score was 0.7 (SD 0.3; -0.3 to 1). Risk factor analysis showed significant revision rates for septic indications (p ≤ 0.001) as well as femoral defect size (p = 0.001). Conclusion. Since large acetabular defects are being treated surgically more often, custom-made PPR should be integrated as an option in treatment algorithms. Monoflange PPR, with primary iliac fixation, offers a viable treatment option for Paprosky III defects with promising functional results, while requiring less soft-tissue exposure and allowing immediate full weightbearing. Cite this article: Bone Jt Open 2024;5(8):688–696

Aims. The management of fractures of the medial epicondyle is one of the greatest controversies in paediatric fracture care, with uncertainty concerning the need for surgery. The British Society of Children’s Orthopaedic Surgery prioritized this as their most important research question in paediatric trauma. This is the protocol for a randomized controlled, multicentre, prospective superiority trial of operative fixation versus nonoperative treatment for displaced medial epicondyle fractures: the Surgery or Cast of the EpicoNdyle in Children’s Elbows (SCIENCE) trial. Methods. Children aged seven to 15 years old inclusive, who have sustained a displaced fracture of the medial epicondyle, are eligible to take part. Baseline function using the Patient-Reported Outcomes Measurement Information System (PROMIS) upper limb score, pain measured using the Wong Baker FACES pain scale, and quality of life (QoL) assessed with the EuroQol five-dimension questionnaire for younger patients (EQ-5D-Y) will be collected. Each patient will be randomly allocated (1:1, stratified using a minimization algorithm by centre and initial elbow dislocation status (i.e. dislocated or not-dislocated at presentation to the emergency department)) to either a regimen of the operative fixation or non-surgical treatment. Outcomes. At six weeks, and three, six, and 12 months, data on function, pain, sports/music participation, QoL, immobilization, and analgesia will be collected. These will also be repeated annually until the child reaches the age of 16 years. Four weeks after injury, the main outcomes plus data on complications, resource use, and school absence will be collected. The primary outcome is the PROMIS upper limb score at 12 months post-randomization. All data will be obtained through electronic questionnaires completed by the participants and/or parents/guardians. The NHS number of participants will be stored to enable future data linkage to sources of routinely collected data (i.e. Hospital Episode Statistics). Cite this article: Bone Jt Open 2024;5(1):69–77

Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407

Aims. Current guidelines consider analyses of joint aspirates, including leucocyte cell count (LC) and polymorphonuclear percentage (PMN%) as a diagnostic mainstay of periprosthetic joint infection (PJI). It is unclear if these parameters are subject to a certain degree of variability over time. Therefore, the aim of this study was to evaluate the variation of LC and PMN% in patients with aseptic revision total knee arthroplasty (TKA). Methods. We conducted a prospective, double-centre study of 40 patients with 40 knee joints. Patients underwent joint aspiration at two different time points with a maximum period of 120 days in between these interventions and without any events such as other joint aspirations or surgeries. The main indications for TKA revision surgery were aseptic implant loosening (n = 24) and joint instability (n = 11). Results. Overall, 80 synovial fluid samples of 40 patients were analyzed. The average time period between the joint aspirations was 50 days (SD 32). There was a significantly higher percentage change in LC when compared to PMN% (44.1% (SD 28.6%) vs 27.3% (SD 23.7%); p = 0.003). When applying standard definition criteria, LC counts were found to skip back and forth between the two time points with exceeding the thresholds in up to 20% of cases, which was significantly more compared to PMN% for the European Bone and Joint Infection Society (EBJIS) criteria (p = 0.001), as well as for Musculoskeletal Infection Society (MSIS) (p = 0.029). Conclusion. LC and PMN% are subject to considerable variation. According to its higher interindividual variance, LC evaluation might contribute to false-positive or false-negative results in PJI assessment. Single LC testing prior to TKA revision surgery seems to be insufficient to exclude PJI. On the basis of the obtained results, PMN% analyses overrule LC measurements with regard to a conclusive diagnostic algorithm. Cite this article: Bone Jt Open 2021;2(8):566–572

Aims. The worldwide COVID-19 pandemic is directly impacting the field of orthopaedic surgery and traumatology with postponed operations, changed status of planned elective surgeries and acute emergencies in patients with unknown infection status. To this point, Germany's COVID-19 infection numbers and death rate have been lower than those of many other nations. Methods. This article summarizes the current regimen used in the field of orthopaedics in Germany during the COVID-19 pandemic. Internal university clinic guidelines, latest research results, expert consensus, and clinical experiences were combined in this article guideline. Results. Every patient, with and without symptoms, should be screened for COVID-19 before hospital admission. Patients should be assigned to three groups (infection status unknown, confirmed, or negative). Patients with unknown infection status should be considered as infectious. Dependent of the infection status and acuity of the symptoms, patients are assigned to a COVID-19-free or affected zone of the hospital. Isolation, hand hygiene, and personal protective equipment is essential. Hospital personnel directly involved in the care of COVID-19 patients should be tested on a weekly basis independently of the presence of clinical symptoms, staff in the COVID-19-free zone on a biweekly basis. Class 1a operation rooms with laminar air flow and negative pressure are preferred for surgery in COVID-19 patients. Electrocautery should only be utilized with a smoke suction system. In cases of unavoidable elective surgery, a self-imposed quarantine of 14 days is recommended prior to hospital admission. Conclusion. During the current COVID-19 pandemic, orthopaedic patients admitted to the hospital should be treated based on an interdisciplinary algorithm, strictly separating infectious and non-infectious cases. Cite this article: Bone Joint Open 2020;1-6:309–315

Aims

Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD. An artificial intelligence (AI) software named HIPPO automatically locates anatomical landmarks on anteroposterior pelvis radiographs and performs the needed measurements. The primary aim of this study was to assess the reliability of this tool as compared to multi-reader evaluation in clinically proven cases of adult HD. The secondary aims were to assess the time savings achieved and evaluate inter-reader assessment.

Aims

Classifying trochlear dysplasia (TD) is useful to determine the treatment options for patients suffering from patellofemoral instability (PFI). There is no consensus on which classification system is more reliable and reproducible for the purpose of guiding clinicians’ management of PFI. There are also concerns about the validity of the Dejour Classification (DJC), which is the most widely used classification for TD, having only a fair reliability score. The Oswestry-Bristol Classification (OBC) is a recently proposed system of classification of TD, and the authors report a fair-to-good interobserver agreement and good-to-excellent intraobserver agreement in the assessment of TD. The aim of this study was to compare the reliability and reproducibility of these two classifications.

Aims

Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre.

Aims

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.