Geometric deep learning is a relatively new field that combines the principles of deep learning with techniques from geometry and topology to analyze data with complex structures, such as graphs and manifolds. In orthopedic research, geometric deep learning has been applied to a variety of tasks, including the analysis of imaging data to detect and classify abnormalities, the prediction of patient outcomes following surgical interventions, and the identification of risk factors for degenerative joint disease. This review aims to summarize the current state of the field and highlight the key findings and applications of geometric deep learning in orthopedic research. The review also discusses the potential benefits and limitations of these approaches and identifies areas for future research. Overall, the use of geometric deep learning in orthopedic research has the potential to greatly advance our understanding of the musculoskeletal system and improve patient care

Objectives. Despite the fact that research fraud and misconduct are under scrutiny in the field of orthopaedic research, little systematic work has been done to uncover and characterise the underlying reasons for academic retractions in this field. The purpose of this study was to determine the rate of retractions and identify the reasons for retracted publications in the orthopaedic literature. Methods. Two reviewers independently searched MEDLINE, EMBASE, and the Cochrane Library (1995 to current) using MeSH keyword headings and the ‘retracted’ filter. We also searched an independent website that reports and archives retracted scientific publications (. www.retractionwatch.com. ). Two reviewers independently extracted data including reason for retraction, study type, journal impact factor, and country of origin. Results. One hundred and ten retracted studies were included for data extraction. The retracted studies were published in journals with impact factors ranging from 0.000 (discontinued journals) to 13.262. In the 20-year search window, only 25 papers were retracted in the first ten years, with the remaining 85 papers retracted in the most recent decade. The most common reasons for retraction were fraudulent data (29), plagiarism (25) and duplicate publication (20). Retracted articles have been cited up to 165 times (median 6; interquartile range 2 to 19). Conclusion. The rate of retractions in the orthopaedic literature is increasing, with the majority of retractions attributed to academic misconduct and fraud. Orthopaedic retractions originate from numerous journals and countries, indicating that misconduct issues are widespread. The results of this study highlight the need to address academic integrity when training the next generation of orthopaedic investigators. Cite this article: J. Yan, A. MacDonald, L-P. Baisi, N. Evaniew, M. Bhandari, M. Ghert. Retractions in orthopaedic research: A systematic review. Bone Joint Res 2016;5:263–268. DOI: 10.1302/2046-3758.56.BJR-2016-0047

Non-linear methods in statistical shape analysis have become increasingly important in orthopedic research as they allow for more accurate and robust analysis of complex shape data such as articulated joints, bony defects and cartilage loss. These methods involve the use of non-linear transformations to describe shapes, rather than the traditional linear approaches, and have been shown to improve the precision and sensitivity of shape analysis in a variety of applications. In orthopedic research, non-linear methods have been used to study a range of topics, including the analysis of bone shape and structure in relation to osteoarthritis, the assessment of joint deformities and their impact on joint function, and the prediction of patient outcomes following surgical interventions. Overall, the use of non-linear methods in statistical shape analysis has the potential to advance our understanding of the relationship between shape and function in the musculoskeletal system and improve the diagnosis and treatment of orthopedic conditions

Background. The economic crisis has significantly altered the quality of life in Greece. The obvious negative impact on the offered social and health services has been adequately analysed. We aimed to determine whether the economic crisis has influenced the quantity and quality of Orthopaedic research in Greece, as mirrored from the papers presented at the annual meetings of Hellenic Association of Orthopaedic Surgery between the years 2008–2014. Methods. The abstracts of the papers (oral and posters) presented in these meetings have been examined. Details regarding the department of origin were registered. We determined the level of evidence (according to the AAOS classification system), found the amount of papers that were published in PubMed journal and noted if a department of another country participated. Results. Through the years 2008–2014 the papers concerning clinical and basic orthopaedic research that were presented were 146, 207, 304, 331, 318, 234 and 191 respectively. The percentage of those that were level 1 studies was approximately 2%, 3%, 2%, 3%, 2%, 4% and 4% respectively. The percentage of level 2 studies was 2%, 1%, 2%, 3%, 1%, 3% and 0%. Level 3 studies were the 10%, 9%, 7%, 7%, 7%, 9% and 6%. The percentage of level 4 studies was 75%, 74%, 60%, 59%, 61%, 65% and 64%. Level 5 studies were the 10%, 17%, 31%, 27%, 28%, 19% and 26%. Each year, the papers that were published in PubMed were 7%, 21%, 13%, 7%, 6%, 6% and 9%. A department of another country participated in 18%, 17%, 18%, 16%, 16%, 21% and 13% of the total of papers. Conclusion. As the economic crisis deepens, the quantity of the presented papers has been reduced. More importantly, the percentage of level 1 and 2 studies has been remained steadily low. Also, we could remark a reduction on the published studies in PubMed journals. These results raise concerns about the potential impact of the crisis in the future. Level of Evidence. IV

The World Health Organisation (WHO) has recently identified musculoskeletal care as a major global health issue in the developing world. However, little is known about the quality and trends of orthopaedic research in resource-poor settings. The purpose of this study was to perform a systematic review of orthopaedic research in low-income countries (LIC). The primary objective was to determine the quality and publication parameters of studies performed in LIC. Secondary objectives sought to provide recommendations for successful strategies to implement research endeavors in LIC. A systematic review of the literature was performed by searching MEDLINE (1966-November 2014), EMBASE and the Cochrane Library to identify peer-reviewed orthopaedic research conducted in LICs. The PRISMA guidelines for performing a systematic review were followed. LIC were defined by the WHO and by the World Bank as countries with gross national income per capita equal or less than 1045US$. Inclusion criteria were (1) studies performed in a LIC, (2) conducted on patients afflicted by an orthopaedic condition, and (3) evaluated either an orthopaedic intervention or outcome. The Oxford Centre for Evidence-Based Medicine Levels of Evidence, and Grading of Recommendations Assessment, Development and Evaluation (GRADE) were used to objectively rate the overall methodological quality of each study. Additional data collected from these studies included the publication year, journal demographics, orthopaedic subspecialty and authors' country of origin. A total of 1,809 articles were screened and 277 studies met our inclusion criteria. Eighty-eight percent of studies conducted in LIC were of lower quality evidence according to the GRADE score and consisted mostly of small case series or case reports. Bangladesh and Nepal were the only two LIC with national journals and produced the highest level of research evidence. Foreign researchers produced over 70% of the studies with no collaboration with local LIC researchers. The most common subspecialties were trauma (42%) and paediatrics (14%). The 3 most frequent countries where the research originated were the United States (42%), United Kingdom (11%), and Canada (8%). The 3 most common locations where research was conducted were Haiti (18%), Afghanistan (14%), and Malawi (7%). The majority of orthopaedic studies conducted in LIC were of lower quality and performed by foreign researchers with little local collaboration. In order to promote the development of global orthopaedic surgery and research in LIC, we recommend (1) improving the collaboration between researchers in developed and LIC, (2) promoting the teaching of higher-quality and more rigorous research methodology through shared partnerships, (3) improving the capacity of orthopaedic research in developing nations through national peer-reviewed journals, and (4) dedicated subsections in international orthopaedic journals to global healthcare research

Scientific truth is an oxymoron. The goal of modern science is an understanding of the natural world. Truth is the goal of empiricism. In orthopaedic research conflict develops between these goals because empiricists seek to discover ways to improve musculoskeletal health and scientists seek to understand how the musculoskeletal system functions. When resources are limited, a hard choice must be made concerning which path to pursue. The conflict actually has a long history in Western culture that can be traced to differences between Greek and Roman approaches to discovering truths about the natural world. For ancient Greeks, no truth was complete unless a cause-and-effect connection could be established following analysis of observations. For Romans, truths were empirical. They were solutions to solved problems; an aqueduct that did not leak or a healed fracture. Empirical approaches to problems have been a characteristic of Homo sapiens since the Stone Age. They defined the original methodology of medicine and so established it as a “truth” profession. The Romans added engineering to the list of truth professions, although they did not classify it as a profession. Engineering and medicine functioned as truth professions until the 20th century. Science was much slower to mature. The term “scientist” was not coined until 1834. It was not established as a distinct profession until it was freed from scholasticism by the natural philosopher, Francis Bacon, who introduced inductive logic, and Galileo Galilei in the 17th century. They and Isaac Newton launched the Enlightenment which stimulated scientific research for over a century. By the mid-19. th. century, progress in science was sufficient to convince many scientists that they were members of a truth profession that would eventually be able to explain all life functions in terms of physics and chemistry. This reductionist view prevailed until 1927, when it was shown to be invalid by Werner Heisenberg. As a consequence of his ‘Principle of Uncertainty’, science is no longer a “truth” profession. Instead, scientific analysis has become a statistical methodology devoid of final proofs. In place of proofs, the scientist must formulate falsifiable hypotheses that are the reverse of those being proposed. In this approach, developed mainly by Karl Popper, observations are analyzed statistically and if they significantly disagree with the falsifiable hypothesis it may be said to have been disproven and one can say that the data support the proposed hypothesis. Members of truth professions began to perform experiments and employ science as an approach to truth in the 20th century thereby developing an “empirical science”. Since these investigators are constrained by their profession to favor truths, they tend to minimize the Greek and maximize the Roman components of their research. The result has been a dichotomy in science between those whose research success is measured by its contribution to “cures” and those whose research success is measured by its contribution to understanding. In orthopaedics, the dichotomy separates analytical scientists from engineers and physicians. In addition, caught in between are the bioengineers. There is a need for better communication between all

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.

Introduction and aims

Robotic Assisted Arthroplasty (RAA) is increasingly proliferative in the international orthopaedic environment. Traditional bibliometric methods poorly assess the impact of surgical innovations such as robotic technology. Progressive Scholarly Acceptance (PSA) is a new model of bibliographic analysis which quantitatively evaluates the impact of robotic technology in the orthopaedic scientific community.

Abstract

The inquisitive and skeptical nature of humans drives research. Questions continue to be raised from a basic, applied and clinical perspective related to our areas of interest—be it molecular biology, biomaterials, biomechanics or clinical. The future of research will only be realised by understanding the past and the planning a pathway for the future. Translating advances in the laboratory to the patient are key to improving clinical outcomes. The future holds great promise, as long as we continue to challenge ourselves and ask those fundamental questions of ‘why’ and ‘how’ things happen.

Summary

Timing for the application and use of fentanyl patches for pre-emptive analgesia and sedation is crucial to obtain good clinical outcomes. Placement and timing is important to maximise clinical effect and apparent levels of analgesia.

Aim: To review the study size and requirements of studies looking at factors affecting outcome following total hip arthroplasty.

Background: The orthopaedic literature is full of claims that new products out-perform older ones, cemented and un-cemented components are equal, cementing grades and mantles are all important and that component orientation is vital to longevity. We are also aware of patients who have performed well despite having numerous adverse features to their joint replacements.

We have searched the available literature for factors that have been implicated in the survival of hip replacements. We have used these to determine the likely study sizes required to provide meaningful data.

Method: We identified over 50 variables that have been implicated in the survival of hip replacements. Assuming all of them to be of equal relevance the study size required for multivariate analysis to be possible would be in excess of 50000. Some of these variables are less likely to be of great significance and this number could potentially be reduced to 25–30000.

Discussion: Because total hip replacement has a greater than 90% success rate at 10 years in nearly all series this makes the numbers required for multivariate analysis much larger. Individual factors affecting survival will be difficult to identify unless large series are considered.

Conclusion: The only way to reduce the numbers required for meaningful studies is to increase the matching of as many variables as possible to ensure that investigators conclusions are valid in the majority of small series orthopaedic studies.

The use of Joint Registers is likely to be the only way of obtaining the volume of data required to detect individual factors affecting survival. Care will still need to be taken interpreting this data as there are still numerous variables which are not accounted for in the Joint Register.

A unit of 12 orthopaedic surgeons serving a population catchment of 180,000 have collaborated to collect prospective data on a wide range of orthopaedic conditions, using well proven internationally validated scoring systems. All patients, rural and urban, public and private, in the region are being enrolled. This project is distinct from but complementary to National Joint Registry data. A benchmarking period of 2 years has been completed, and now prospective trials are being commenced. 4000 patient datasets have been obtained to date.

We report on the logistics of establishing a regional research program in a medium-sized New Zealand centre, and results achieved to date. We present our experience with a view to encouraging other centres to consider similar ventures.

For decades, universities and research centers have been applying modeling and simulation (M&S) to problems involving health and medicine, coining the expression in silico clinical trials. However, its use is still limited to a restricted pool of specialists.

It is here proposed an easy-to-use cloud-based platform that aims to create a collaborative marketplace for M&S in orthopedics, where developers and model creators are able to capitalize on their work while protecting their intellectual property (IP), and researcher, surgeons and medical device companies can use M&S to accelerate time and to reduce costs of their research and development (R&D) processes.

Digital libraries on InSilicoTrials.com are built on collaborations among first-rate research center, model developers, software, and cloud providers (partners). Their access is provided to life science and healthcare companies, clinical centers, and research institutes (users), offering them with several solutions for the different steps of the orthopedics and medical devices R&D process. The platform is built using the Microsoft Azure cloud services, conforming to global standards of security and privacy for healthcare, ensuring that clinical data is properly managed, protected, and kept private. The environment protects the IP of partners against the downloading, copying, and changing of their M&S solutions; while providing a safe environment for users to seamlessly upload their own data, set up and run simulations, analyze results, and produce reports in conformity with regulatory requirements.

The proposed platform allows exploitation of M&S through a Software-as-a-Service delivery model. The pay-per-use pricing: 1. provide partners with a strong incentive to commercialize their high-quality M&S solutions; 2. enable users with limited budget, such as small companies, research centers and hospitals, to use advanced M&S solutions. Pricing of the M&S tools is based on specific aspects, such as particular features and computational power required, in agreement with the developing partner, and is distinct for different types of customers (i.e., academia or industry).

The first medical devices application hosted on InSilicoTrials.com is NuMRis (Numerical Magnetic Resonance Implant Safety), implemented in collaboration with the U.S. F.D.A. Center for Devices and Radiological Health, and ANSYS, Inc. The automatic tool allows the investigation of radiofrequency (RF)-induced heating of passive medical implants, such as orthopedic devices (e.g., rods and screws), pain management devices (e.g., leads), and cardiovascular devices (e.g., stents), following the ASTM F2182-19e2 Standard Test Method. NuMRis promotes the broader adoption of digital evidence in preclinical trials for RF safety analysis, supporting the device submission process and pre-market regulatory evaluation.

InSilicoTrials.com aims at defining a new collaborative framework in healthcare, engaging research centers to safely commercialize their IP, i.e., model templates, simulation tools and virtual patients, by helping clinicians and healthcare companies to significantly expedite the pre-clinical and clinical development phases, and to move across the regulatory approval and HTA processes.

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.

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

Aims. The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons. Methods. Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes. Results. The four intersecting themes identified were: 1) validity in traditional research, 2) confusion around the definition of AI, 3) an inability to validate AI research, and 4) cautious optimism about AI research. Underpinning these themes is the notion of a validity heuristic that is strongly rooted in traditional research teaching and embedded in medical and surgical training. Conclusion. Research involving AI sometimes challenges the accepted traditional evidence-based framework. This can give rise to confusion among orthopaedic surgeons, who may be unable to confidently validate findings. In our study, the impact of this was mediated by cautious optimism based on an ingrained validity heuristic that orthopaedic surgeons develop through their medical training. Adding to this, the integration of AI into everyday life works to reduce suspicion and aid acceptance. Cite this article: Bone Jt Open 2023;4(9):696–703