Aims. The aim of this systematic review and meta-analysis was to gather epidemiological information on selected musculoskeletal injuries and to provide pooled injury-specific incidence rates. Methods. PubMed (National Library of Medicine) and Scopus (Elsevier) databases were searched. Articles were eligible for inclusion if they reported incidence rate (or count with population at risk), contained data on adult population, and were written in English language. The number of cases and population at risk were collected, and the pooled incidence rates (per 100,000 person-years) with 95% confidence intervals (CIs) were calculated by using either a fixed or random effects model. Results. The screening of titles yielded 206 articles eligible for inclusion in the study. Of these, 173 (84%) articles provided sufficient information to be included in the pooled incidence rates. Incidences of fractures were investigated in 154 studies, and the most common fractures in the whole adult population based on the pooled incidence rates were distal radius fractures (212.0, 95% CI 178.1 to 252.4 per 100,000 person-years), finger fractures (117.1, 95% CI 105.3 to 130.2 per 100,000 person-years), and hip fractures (112.9, 95% CI 82.2 to 154.9 per 100,000 person-years). The most common sprains and dislocations were ankle sprains (429.4, 95% CI 243.0 to 759.0 per 100,000 person-years) and first-time patellar dislocations (32.8, 95% CI 21.6 to 49.7 per 100,000 person-years). The most common injuries were anterior cruciate ligament (17.5, 95% CI 6.0 to 50.2 per 100,000 person-years) and Achilles (13.7, 95% CI 9.6 to 19.5 per 100,000 person-years) ruptures. Conclusion. The presented pooled incidence estimates serve as important references in assessing the global economic and social burden of musculoskeletal injuries. Cite this article: Bone Joint Res 2022;11(11):814–825

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

Aims. Biofilm-related infection is a major complication that occurs in orthopaedic surgery. Various treatments are available but efficacy to eradicate infections varies significantly. A systematic review was performed to evaluate therapeutic interventions combating biofilm-related infections on in vivo animal models. Methods. Literature research was performed on PubMed and Embase databases. Keywords used for search criteria were “bone AND biofilm”. Information on the species of the animal model, bacterial strain, evaluation of biofilm and bone infection, complications, key findings on observations, prevention, and treatment of biofilm were extracted. Results. A total of 43 studies were included. Animal models used included fracture-related infections (ten studies), periprosthetic joint infections (five studies), spinal infections (three studies), other implant-associated infections, and osteomyelitis. The most common bacteria were Staphylococcus species. Biofilm was most often observed with scanning electron microscopy. The natural history of biofilm revealed that the process of bacteria attachment, proliferation, maturation, and dispersal would take 14 days. For systemic mono-antibiotic therapy, only two of six studies using vancomycin reported significant biofilm reduction, and none reported eradication. Ten studies showed that combined systemic and topical antibiotics are needed to achieve higher biofilm reduction or eradication, and the effect is decreased with delayed treatment. Overall, 13 studies showed promising therapeutic potential with surface coating and antibiotic loading techniques. Conclusion. Combined topical and systemic application of antimicrobial agents effectively reduces biofilm at early stages. Future studies with sustained release of antimicrobial and biofilm-dispersing agents tailored to specific pathogens are warranted to achieve biofilm eradication. Cite this article: Bone Joint Res 2022;11(10):700–714

Aims. The aim of this study was to review the current evidence surrounding curve type and morphology on curve progression risk in adolescent idiopathic scoliosis (AIS). Methods. A comprehensive search was conducted by two independent reviewers on PubMed, Embase, Medline, and Web of Science to obtain all published information on morphological predictors of AIS progression. Search items included ‘adolescent idiopathic scoliosis’, ‘progression’, and ‘imaging’. The inclusion and exclusion criteria were carefully defined. Risk of bias of studies was assessed with the Quality in Prognostic Studies tool, and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. In all, 6,286 publications were identified with 3,598 being subjected to secondary scrutiny. Ultimately, 26 publications (25 datasets) were included in this review. Results. For unbraced patients, high and moderate evidence was found for Cobb angle and curve type as predictors, respectively. Initial Cobb angle > 25° and thoracic curves were predictive of curve progression. For braced patients, flexibility < 28% and limited in-brace correction were factors predictive of progression with high and moderate evidence, respectively. Thoracic curves, high apical vertebral rotation, large rib vertebra angle difference, small rib vertebra angle on the convex side, and low pelvic tilt had weak evidence as predictors of curve progression. Conclusion. For curve progression, strong and consistent evidence is found for Cobb angle, curve type, flexibility, and correction rate. Cobb angle > 25° and flexibility < 28% are found to be important thresholds to guide clinical prognostication. Despite the low evidence, apical vertebral rotation, rib morphology, and pelvic tilt may be promising factors. Cite this article: Bone Joint J 2022;104-B(4):424–432

Aims. The follow-up interval of a study represents an important aspect that is frequently mentioned in the title of the manuscript. Authors arbitrarily define whether the follow-up of their study is short-, mid-, or long-term. There is no clear consensus in that regard and definitions show a large range of variation. It was therefore the aim of this study to systematically identify clinical research published in high-impact orthopaedic journals in the last five years and extract follow-up information to deduce corresponding evidence-based definitions of short-, mid-, and long-term follow-up. Methods. A systematic literature search was performed to identify papers published in the six highest ranked orthopaedic journals during the years 2015 to 2019. Follow-up intervals were analyzed. Each article was assigned to a corresponding subspecialty field: sports traumatology, knee arthroplasty and reconstruction, hip-preserving surgery, hip arthroplasty, shoulder and elbow arthroplasty, hand and wrist, foot and ankle, paediatric orthopaedics, orthopaedic trauma, spine, and tumour. Mean follow-up data were tabulated for the corresponding subspecialty fields. Comparison between means was conducted using analysis of variance. Results. Of 16,161 published articles, 590 met the inclusion criteria. Of these, 321 were of level IV evidence, 176 level III, 53 level II, and 40 level I. Considering all included articles, a long-term study published in the included high impact journals had a mean follow-up of 151.6 months, a mid-term study of 63.5 months, and a short-term study of 30.0 months. Conclusion. The results of this study provide evidence-based definitions for orthopaedic follow-up intervals that should provide a citable standard for the planning of clinical studies. A minimum mean follow-up of a short-term study should be 30 months (2.5 years), while a mid-term study should aim for a mean follow-up of 60 months (five years), and a long-term study should aim for a mean of 150 months (12.5 years). Level of Evidence: Level I. Cite this article: Bone Jt Open 2021;2(5):344–350

Aims. The effect of the gut microbiota (GM) and its metabolite on bone health is termed the gut-bone axis. Multiple studies have elucidated the mechanisms but findings vary greatly. A systematic review was performed to analyze current animal models and explore the effect of GM on bone. Methods. Literature search was performed on PubMed and Embase databases. Information on the types and strains of animals, induction of osteoporosis, intervention strategies, determination of GM, assessment on bone mineral density (BMD) and bone quality, and key findings were extracted. Results. A total of 30 studies were included, of which six studies used rats and 24 studies used mice. Osteoporosis or bone loss was induced in 14 studies. Interventions included ten with probiotics, three with prebiotics, nine with antibiotics, two with short-chain fatty acid (SCFA), six with vitamins and proteins, two with traditional Chinese medicine (TCM), and one with neuropeptide Y1R antagonist. In general, probiotics, prebiotics, nutritional interventions, and TCM were found to reverse the GM dysbiosis and rescue bone loss. Conclusion. Despite the positive therapeutic effect of probiotics, prebiotics, and nutritional or pharmaceutical interventions on osteoporosis, there is still a critical knowledge gap regarding the role of GM in rescuing bone loss and its related pathways. Cite this article: Bone Joint Res 2021;10(1):51–59

Aims. This study evaluates the quality of patient-reported outcome measures (PROMs) reported in childhood fracture trials and recommends outcome measures to assess and report physical function, functional capacity, and quality of life using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) standards. Methods. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic review of OVID Medline, Embase, and Cochrane CENTRAL was performed to identify all PROMs reported in trials. A search of OVID Medline, Embase, and PsycINFO was performed to identify all PROMs with validation studies in childhood fractures. Development studies were identified through hand-searching. Data extraction was undertaken by two reviewers. Study quality and risk of bias was evaluated by COSMIN guidelines and recorded on standardized checklists. Results. Searches yielded 13,672 studies, which were screened to identify 124 trials and two validation studies. Review of the 124 trials identified 16 reported PROMs, of which two had validation studies. The development papers were retrieved for all PROMs. The quality of the original development studies was adequate for Patient-Reported Outcomes Measurement Information System (PROMIS) Mobility and Upper Extremity and doubtful for the EuroQol Five Dimension Youth questionnaire (EQ-5D-Y). All other PROMs were found to have inadequate development studies. No content validity studies were identified. Reviewer-rated content validity was acceptable for six PROMs: Activity Scale for Kids (ASK), Childhood Health Assessment Questionnaire, PROMIS Upper Extremity, PROMIS Mobility, EQ-5D-Y, and Pediatric Quality of Life Inventory (PedsQL4.0). The Modified Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire was shown to have indeterminate reliability and convergence validity in one study and PROMIS Upper Extremity had insufficient convergence validity in one study. Conclusion. There is insufficient evidence to recommend strongly the use of any single PROM to assess and report physical function or quality of life following childhood fractures. There is a need to conduct validation studies for PROMs. In the absence of these studies, we cautiously recommend the use of the PROMIS or ASK-P for physical function and the PedsQL4.0 or EQ-5D-Y for quality of life. Cite this article: Bone Joint J 2020;102-B(12):1599–1607

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Lower limb reconstruction (LLR) has a profound impact on patients, affecting multiple areas of their lives. Many patient-reported outcome measures (PROMs) are employed to assess these impacts; however, there are concerns that they do not adequately capture all outcomes important to patients, and may lack content validity in this context. This review explored whether PROMs used with adults requiring, undergoing, or after undergoing LLR exhibited content validity and adequately captured outcomes considered relevant and important to patients.

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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.

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The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA).

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The aim of this study was to report the pooled prevalence of post-traumatic osteoarthritis (PTOA) and examine whether the risk of developing PTOA after anterior cruciate ligament (ACL) injury has decreased in recent decades.

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Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as gait analysis, to delineate a patient’s functional capacity and customize surgical interventions, may address these shortcomings. This systematic review endeavours to investigate the application of objective functional assessments in appraising individuals undergoing THA.

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This systematic review aimed to summarize the full range of complications reported following ankle arthroscopy and the frequency at which they occur.

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To systematically review the efficacy of split tendon transfer surgery on gait-related outcomes for children and adolescents with cerebral palsy (CP) and spastic equinovarus foot deformity.

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The aim of this study was to perform a systematic review of the evidence for the use of intraoperative cell salvage in patients undergoing revision hip arthroplasty, and specifically to analyze the available data in order to quantify any associated reduction in the use of allogenic blood transfusion, and the volume which is used.

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Intra-articular (IA) injection may be used when treating hip osteoarthritis (OA). Common injections include steroids, hyaluronic acid (HA), local anaesthetic, and platelet-rich plasma (PRP). Network meta-analysis allows for comparisons between two or more treatment groups and uses direct and indirect comparisons between interventions. This network meta-analysis aims to compare the efficacy of various IA injections used in the management of hip OA with a follow-up of up to six months.

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This meta-analysis and systematic review aimed to comprehensively investigate the effects of vitamin K supplementation on bone mineral density (BMD) at various sites and bone metabolism in middle-aged and older adults.

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The aims of this study were to identify and evaluate the current literature examining the prognostic factors which are associated with failure of total elbow arthroplasty (TEA).

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

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To analyze whether the addition of risk-based criteria to clinical examination-based selective ultrasound screening would increase the rates of early detected cases of developmental dysplasia of the hip (DDH) and decrease the rate of late detected cases.