Aims. Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis. Methods. We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases for studies applying ML algorithms in TSA. The analysis focused on dataset characteristics, relevant subspecialties, specific ML algorithms used, and their performance outcomes. Results. Following the final screening process, 25 articles satisfied the eligibility criteria for our review. Of these, 60% focused on tabular data while the remaining 40% analyzed image data. Among them, 16 studies were dedicated to developing new models and nine used transfer learning to leverage existing pretrained models. Additionally, three of these models underwent external validation to confirm their reliability and effectiveness. Conclusion. ML algorithms used in TSA demonstrated fair to good performance, as evidenced by the reported metrics. Integrating these models into daily clinical practice could revolutionize TSA, enhancing both surgical precision and patient outcome predictions. Despite their potential, the lack of transparency and generalizability in many current models poses a significant challenge, limiting their clinical utility. Future research should prioritize addressing these limitations to truly propel the field forward and maximize the benefits of ML in enhancing patient care. Cite this article: Bone Jt Open 2025;6(2):126–134

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The aim of this study was to evaluate the suitability, against an accepted international standard, of a linked hip fracture registry and routinely collected administrative dataset in England to embed and deliver randomized controlled trials (RCTs).

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We estimated the prevalence of people living with at least one hip, knee, or shoulder arthroplasty in the Netherlands.

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The Peri-Implant and PeriProsthetic Survival AnalysiS (PIPPAS) study aimed to investigate the risk factors for one-year mortality of femoral peri-implant fractures (FPIFs).

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Around the world, the emergence of robotic technology has improved surgical precision and accuracy in total knee arthroplasty (TKA). This territory-wide study compares the results of various robotic TKA (R-TKA) systems with those of conventional TKA (C-TKA) and computer-navigated TKA (N-TKA).

Aims. The aims of this study were to develop an automatic system capable of calculating four radiological measurements used in the diagnosis and monitoring of cerebral palsy (CP)-related hip disease, and to demonstrate that these measurements are sufficiently accurate to be used in clinical practice. Methods. We developed a machine-learning system to automatically measure Reimer’s migration percentage (RMP), acetabular index (ACI), head shaft angle (HSA), and neck shaft angle (NSA). The system automatically locates points around the femoral head and acetabulum on pelvic radiographs, and uses these to calculate measurements. The system was evaluated on 1,650 pelvic radiographs of children with CP (682 females and 968 males, mean age 8.3 years (SD 4.5)). Each radiograph was manually measured by five clinical experts. Agreement between the manual clinical measurements and the automatic system was assessed by mean absolute deviation (MAD) from the mean manual measurement, type 1 and type 2 intraclass correlation coefficients (ICCs), and a linear mixed-effects model (LMM) for assessing bias. Results. The MAD scores were 5.7% (SD 8.5%) for RMP, 4.3° (SD 5.4°) for ACI, 5.0° (SD 5.2°) for NSA, and 5.7° (SD 6.1°) for HSA. Overall ICCs quantifying the agreement between the mean manual measurement and the automatic results were 0.91 for RMP, 0.66 for ACI, 0.85 for NSA, and 0.73 for HSA. The LMM showed no statistically significant bias. Conclusion. The results showed excellent agreement between the manual and automatic measurements for RMP, good agreement for NSA, and moderate agreement for HSA and ACI. The performance of the system is sufficient for application in clinical practice to support the assessment of hip migration based on RMP. The system has the potential to save clinicians time and to improve patient care by enabling more comprehensive, consistent, and reliable monitoring of hip migration in children with CP. Cite this article: Bone Joint J 2025;107-B(1):124–132

Aims. Systematic reviews of randomized controlled trials (RCTs) are the highest level of evidence used to inform patient care. However, it has been suggested that the quality of randomization in RCTs in orthopaedic surgery may be low. This study aims to describe the quality of randomization in trials included in systematic reviews in orthopaedic surgery. Methods. Systematic reviews of RCTs testing orthopaedic procedures published in 2022 were extracted from PubMed, Embase, and the Cochrane Library. A random sample of 100 systematic reviews was selected, and all included RCTs were retrieved. To be eligible for inclusion, systematic reviews must have tested an orthopaedic procedure as the primary intervention, included at least one study identified as a RCT, been published in 2022 in English, and included human clinical trials. The Cochrane Risk of Bias-2 Tool was used to assess random sequence generation as ‘adequate’, ‘inadequate’, or ‘no information’; we then calculated the proportion of trials in each category. We also collected data to test the association between these categories and characteristics of the RCTs and systematic reviews. Results. We included 917 unique RCTs. We found that 374 RCTs (40.8%) reported adequate sequence generation, 61 (6.7%) were inadequate, 410 (44.7%) lacked information, and 72 (7.9%) were observational studies incorrectly included as RCTs within the systematic review. Publication year, an author with statistical or epidemiological qualifications, and journal impact factor were each associated with adequate randomization. We found that 45 systematic reviews (45%) included at least one inadequately randomized RCT or an observational study incorrectly treated as a RCT. Conclusion. There is evidence of a lack of random allocation in RCTs included in systematic reviews in orthopaedic surgery. The conduct of RCTs and systematic reviews should be improved to minimize the risk of bias from inadequate randomization in RCTs and mislabelling of non-randomized studies as RCTs. Cite this article: Bone Jt Open 2024;5(12):1072–1080

The December 2024 Research Roundup³⁶⁰ looks at: Skeletal muscle composition, power, and mitochondrial energetics in older men and women with knee osteoarthritis; Machine-learning models to predict osteonecrosis in patients with femoral neck fractures undergoing internal fixation; Aetiology of patient dissatisfaction following primary total knee arthroplasty in the era of robotic-assisted technology; Efficacy and safety of commonly used thromboprophylaxis agents following hip and knee arthroplasty; The COVID-19 effect continues; Nickel allergy in knee arthroplasty: does self-reported sensitivity affect outcomes?; Tranexamic acid use and joint infection risk in total hip and knee arthroplasty.

Hip fractures pose a major global health challenge, leading to high rates of morbidity and mortality, particularly among the elderly. With an ageing population, the incidence of these injuries is rising, exerting significant pressure on healthcare systems worldwide. Despite substantial research aimed at establishing best practice, several key areas remain the subject of ongoing debate. This article examines the latest evidence on the place of arthroplasty in the surgical treatment of hip fractures, with a particular focus on the choice of implant, the use of cemented versus uncemented fixation, and advances in perioperative care.

Cite this article: Bone Joint J 2024;106-B(12):1372–1376.

Periprosthetic joint infection (PJI) represents a complex challenge in orthopaedic surgery associated with substantial morbidity and healthcare expenditures. The debridement, antibiotics, and implant retention (DAIR) protocol is a viable treatment, offering several advantages over exchange arthroplasty. With the evolution of treatment strategies, considerable efforts have been directed towards enhancing the efficacy of DAIR, including the development of a phased debridement protocol for acute PJI management. This article provides an in-depth analysis of DAIR, presenting the outcomes of single-stage, two-stage, and repeated DAIR procedures. It delves into the challenges faced, including patient heterogeneity, pathogen identification, variability in surgical techniques, and antibiotics selection. Moreover, critical factors that influence the decision-making process between single- and two-stage DAIR protocols are addressed, including team composition, timing of the intervention, antibiotic regimens, and both anatomical and implant-related considerations. By providing a comprehensive overview of DAIR protocols and their clinical implications, this annotation aims to elucidate the advancements, challenges, and potential future directions in the application of DAIR for PJI management. It is intended to equip clinicians with the insights required to effectively navigate the complexities of implementing DAIR strategies, thereby facilitating informed decision-making for optimizing patient outcomes.

Cite this article: Bone Joint J 2024;106-B(12):1377–1384.

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The aim of this audit was to assess and improve the completeness and accuracy of the National Joint Registry (NJR) dataset for arthroplasty of the elbow.

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Calcaneal osteomyelitis remains a difficult condition to treat with high rates of recurrence and below-knee amputation, particularly in the presence of severe soft-tissue destruction. This study assesses the outcomes of single-stage orthoplastic surgical treatment of calcaneal osteomyelitis with large soft-tissue defects.

Aims. A review of the literature on elbow replacement found no consistency in the clinical outcome measures which are used to assess the effectiveness of interventions. The aim of this study was to define core outcome domains for elbow replacement. Methods. A real-time Delphi survey was conducted over four weeks using outcomes from a scoping review of 362 studies on elbow replacement published between January 1990 and February 2021. A total of 583 outcome descriptors were rationalized to 139 unique outcomes. The survey consisted of 139 outcomes divided into 18 domains. The readability and clarity of the survey was determined by an advisory group including a patient representative. Participants were able to view aggregated responses from other participants in real time and to revisit their responses as many times as they wished during the study period. Participants were able to propose additional items for inclusion. A Patient and Public Inclusion and Engagement (PPIE) panel considered the consensus findings. Results. A total of 45 respondents completed the survey. Nine core mandatory domains were identified: ‘return to work or normal daily role’; delivery of care was measured in the domains ‘patient satisfaction with the outcome of surgery’ and ‘would the patient have the same operation again’; ‘pain’; ‘revision’; ‘elbow function’; ‘independence in activities of daily living’; ‘health-related quality of life’; and ‘adverse events’. ‘Elbow range of motion’ was identified as important by consensus but was felt to be less relevant by the PPIE panel. The PPIE panel unanimously stated that pain should be used as the primary outcome domain. Conclusion. This study defined core domains for the clinical outcomes of elbow replacement obtained by consensus from patients, carers, and healthcare professionals. Pain may be used as the primary outcome in future studies, where appropriate. Further work is required to define the instruments that should be used. Cite this article: Bone Joint J 2024;106-B(11):xxx–xxx

Aims. The aim of this study was to review the provision of total elbow arthroplasties (TEAs) in England, including the incidence, the characteristics of the patients and the service providers, the types of implant, and the outcomes. Methods. We analyzed the primary TEAs recorded in the National Joint Registry (NJR) between April 2012 and December 2022, with mortality data from the Civil Registration of Deaths dataset. Linkage with Hospital Episode Statistics-Admitted Patient Care (HES-APC) data provided further information not collected by the NJR. The incidences were calculated using estimations of the populations from the Office for National Statistics. The annual number of TEAs performed by surgeons and hospitals was analyzed on a national and regional basis. Results. A total of 3,891 primary TEAs were included. The annual incidence of TEA was between 0.72 and 0.82 per 100,000 persons before 2020 and declined to 0.4 due to a decrease in elective TEAs during the COVID-19 pandemic, with a slight recovery in 2022. Older patients, those of white ethnicity and females, were more likely to undergo TEA. Those who underwent elective TEA had a median wait of between 89 (IQR 41 to 221) and 122 days (IQR 74 to 189) in the years before 2021, and this increased to 183 days (IQR 66 to 350) in 2021. The number of TEAs performed by surgeons per annum remained unchanged, with a median of two (IQR 1 to 3). The median annual number of TEAs per region was three to six times higher than the median annual case load of the highest volume hospital in a region. Patients in the lowest socioeconomic group had a higher rate of serious adverse events and mortality (11%) when undergoing TEA for acute trauma. Conclusion. In England, TEA is more common in older age groups, those of white ethnicity, and females. The COVID-19 pandemic affected the incidence of elective TEA and waiting times, and the provision of TEA has not yet recovered. The Getting it Right First Time recommendation of centralizing services to one centre per region could result in up to a six-fold increase in the number of TEAs being performed in some centres. Cite this article: Bone Joint J 2024;106-B(11):1312–1320

Aims. The evidence base within trauma and orthopaedics has traditionally favoured quantitative research methodologies. Qualitative research can provide unique insights which illuminate patient experiences and perceptions of care. Qualitative methods reveal the subjective narratives of patients that are not captured by quantitative data, providing a more comprehensive understanding of patient-centred care. The aim of this study is to quantify the level of qualitative research within the orthopaedic literature. Methods. A bibliometric search of journals’ online archives and multiple databases was undertaken in March 2024, to identify articles using qualitative research methods in the top 12 trauma and orthopaedic journals based on the 2023 impact factor and SCImago rating. The bibliometric search was conducted and reported in accordance with the preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO). Results. Of the 7,201 papers reviewed, 136 included qualitative methods (0.1%). There was no significant difference between the journals, apart from Bone & Joint Open, which included 21 studies using qualitative methods, equalling 4% of its published articles. Conclusion. This study demonstrates that there is a very low number of qualitative research papers published within trauma and orthopaedic journals. Given the increasing focus on patient outcomes and improving the patient experience, it may be argued that there is a requirement to support both quantitative and qualitative approaches to orthopaedic research. Combining qualitative and quantitative methods may effectively address the complex and personal aspects of patients’ care, ensuring that outcomes align with patient values and enhance overall care quality

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The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs.

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222