Objectives Previous studies suggest Ireland has the smallest osteoporosis treatment in Europe and very little inappropriate prescribing, in contrast to our experience. In this study, we examine the osteoporosis treatment gap in Ireland by assessing the prevalence of appropriate and inappropriate prescribing in 2 subgroups of the Irish dual-energy X-ray absorptiometry (DXA) Health Informatics Prediction (HIP) Project. Treatment eligibility was defined using established intervention thresholds, including prior fracture, femoral-neck T-score ≤−2.5, glucocorticoid use, or Fracture Risk Assessment Tool (FRAX) major osteoporotic fracture risk ≥20% or hip fracture risk ≥3%.Design Secondary cross-sectional analysis of a subgroup of the DXA HIP Project Cohort.Setting 3 hospitals in the West of Ireland. DXA referrals come from primary care providers, hospital consultants and the osteoporosis service.Participants 5564 participants of a previously described convenience cohort including: (i) 3474 subjects referred for a DXA scan, and (ii) 2090 patients who completed a DXA scan.Results 82.4% were female with a mean age of 66.6 years, 59.6% of whom had a prior fracture. Prescribing data of calcium and vitamin D were available for 3738 (67.2%) subjects, and osteoporosis medication for 4157 (74.7%) subjects. Prescribing information was available for more than 99% of the DXA group, but just over 50% of the referral group. When examined in aggregate, the treatment gap is 6% for calcium and vitamin D and 38% for osteoporosis medication, in line with prior publications. However, among those with prescribing information and at least one indication for treatment, only 58.3% were prescribed calcium and vitamin D and 39.1% an osteoporosis medication. Furthermore, among patients without a clear indication for treatment, 50.6% were prescribed calcium and vitamin D, and 32.5% an osteoporosis medication.Conclusions These data suggest the majority of patients with osteoporosis or at high risk of fracture in Ireland today do not receive appropriate osteoporosis treatment, while inappropriate prescribing is substantial. These findings suggest that the true treatment gap in Ireland is substantially larger than aggregate estimates imply.

In this paper, we analyze the interoperability landscape for materials and manufacturing in a broad sense and with a particular focus in the context of data. To set the stage and give an overview of the various facets of this topic, we collect and compare existing definitions and classifications of interoperability (its types, layers, levels) and summarize recommendations from various entities and communities. After this, we carry out an analysis on a set of interoperability scenarios, propose a broad structure of requirements for interoperability, and list some key components that can be used to meet these requirements, with a particular emphasis on the role of semantic technologies and knowledge representation. Finally, we highlight future challenges and suggest directions for best practices. Throughout the paper, we emphasize common points and differences in the landscape. Through this process, relevant dimensions are identified, and various tables are provided with useful syntheses and structured categorizations that can serve as a base for future theoretical work, as well as immediate practical guidance (e.g., for requirements gathering, literature navigation).

DXA technology is widely available today in many regions of the world. There is a growing realization of the value of DXA not only for osteoporosis management but also for sports medicine, sarcopenia, and the assessment of cardiovascular disease and mortality. Such features may be of particular interest for populations with a greater risk of these outcomes such as those with diabetes mellitus or rheumatoid arthritis. Recent systematic reviews and meta-analyses show DXA can robustly predict fractures, cardiovascular disease, dementia and mortality. Rheumatoid arthritis (RA) is a chronic inflammatory disease affecting multiple organs including synovial joints, bone and other tissues. People suffering from RA have a greater propensity to osteoporotic fracture, cardiovascular disease, infection and premature death, which is well recognised. RA is the only unique disease included in some fracture risk algorithms such as FRAX, and so RA patients are often referred for a DXA scan to evaluate their risk of osteoporosis. We have previously shown vertebral fractures, aortic calcification and cardiovascular disease are prevalent in our RA population, with strong association. In this paper we performed a scoping review of published literature in Medline and Embase to better understand the current status of DXA and cardiovascular disease in RA populations. 822 papers were identified in an initial search of which 7 papers reflecting 2,038 RA patients from 7 different countries were included. Study design included 4 cross-sectional, 2 longitudinal and 1 case-control. All included associations with various cardiovascular measures, while only 1 included clinical events as an outcome. Our results suggest this is an area which remains relatively unexplored but has substantial important clinical potential.

The integration of heterogeneous and unstructured data in Industry 4.0, poses a significant challenge, particularly with advanced manufacturing techniques. To address this issue, Knowledge Graphs (KGs) have emerged as a pivotal technology, yet their deployment often encounters the problem of incompletion due to data diversity and diverse storage formats. This study tackles the challenge of KG completion by applying and evaluating state-of-the-art KG embedding models—ComplEx, DistMult, TransE, ConvKB, and ConvE—within a football manufacturing production line context. Our analysis employs two principal metrics of Mean Reciprocal Rank (MRR) and Hits@N (Hits@10, Hits@3, and Hits@1) to comprehensively assess model performance. Our findings reveal that TransE significantly outperforms its counterparts, achieving an average accuracy of 91%, closely followed by ComplEx and DistMult with accuracies of 87% and 84%, respectively. Conversely, ConvKB and ConvE exhibit lower performance levels, with accuracy values of 79% and 76%. Through rigorous statistical testing, including t-tests, meaningful differences in MRR values across the models have been observed, with TransE leading in MRR and ConvE at the lower end of the spectrum. Our research not only sheds light on the efficacy of various KG embedding models in managing tree-like structured datasets within the manufacturing domain but also offers insights into optimising KGs for improved integration and analysis of data in production lines. These contributions are valuable both from academic research in KG completion and industrial practices aiming to enhance production efficiency and data coherence in advanced manufacturing settings.

BACKGROUND:Osteoporotic fractures are a major global public health issue, leading to patient suffering and death, and considerable healthcare costs. Bone mineral density (BMD) measurement is important to identify those with osteoporosis and assess their risk of fracture. Both the absolute BMD and the change in BMD over time contribute to fracture risk. Predicting future fracture in individual patients is challenging and impacts clinical decisions such as when to intervene or repeat BMD measurement. Although the importance of BMD change is recognised, an effective way to incorporate this marginal effect into clinical algorithms is lacking. METHODS:We compared two methods using longitudinal DXA data generated from subjects with two or more hip DXA scans on the same machine between 2000 and 2018. A simpler statistical method (ZBM) was used to predict an individual's future BMD based on the mean BMD and the standard deviation of the reference group and their BMD measured in the latest scan. A more complex deep learning (DL)-based method was developed to cope with multidimensional longitudinal data, variables extracted from patients' historical DXA scan(s), as well as features drawn from the ZBM method. Sensitivity analyses of several subgroups was conducted to evaluate the performance of the derived models. RESULTS:2948 white adults aged 40-90 years met our study inclusion: 2652 (90 %) females and 296 (10 %) males. Our DL-based models performed significantly better than the ZBM models in women, particularly our Hybrid-DL model. In contrast, the ZBM-based models performed as well or better than DL-based models in men. CONCLUSIONS:Deep learning-based and statistical models have potential to forecast future BMD using longitudinal clinical data. These methods have the potential to augment clinical decisions regarding when to repeat BMD testing in the assessment of osteoporosis.

One of the greatest challenges in creating effective decision-making systems for connected enterprises is the management of cross-domain information. In manufacturing value networks where supply chains are increasingly intertwined, and closed-loop lifecycle management requires traversing several domains, ontologies are proving to be a reliable reference for cross-domain semantic interoperability. However, ontology development, implementation, and management are fragmented and difficult for new users of ontologies to grasp. This is a significant challenge in environments where ontologies are vital for managing effective data exchanges in complex industrial processes. The OntoCommons project has evolved an ontology ecosystem that aims to lower the entry barrier to using ontologies. Building on this ambition, we present a holistic approach to the integration and management of ontologies horizontally across manufacturing ecosystems, including the creation of reference documentation for manufacturing value networks and related standards, available tools for working with ontologies, and examples of vertical integration of knowledge from application level with domain-level and top-level ontology reference documentation. As a novel research direction, we propose a meta-level approach to ontology-driven knowledge management in manufacturing ecosystems. Based on evidence from recent breakthroughs, we present future and emerging research directions.

Industry 4.0 (I4.0) is a new era in the industrial revolution that emphasizes machine connectivity, automation, and data analytics. The I4.0 pillars such as autonomous robots, cloud computing, horizontal and vertical system integration, and the industrial internet of things have increased the performance and efficiency of production lines in the manufacturing industry. Over the past years, efforts have been made to propose semantic models to represent the manufacturing domain knowledge, one such model is Reference Generalized Ontological Model (RGOM).11 https://w3id.org/rgom However, its adaptability like other models is not ensured due to the lack of manufacturing data. In this paper, we aim to develop a benchmark dataset for knowledge graph generation in Industry 4.0 production lines and to show the benefits of using ontologies and semantic annotations of data to showcase how the I4.0 industry can benefit from KGs and semantic datasets. This work is the result of collaboration with the production line managers, supervisors, and engineers in the football industry to acquire realistic production line data22 https://github.com/MuhammadYahta/ManufacturingProductionLineDataSetGeneration-Football,.33 https://zenodo.org/record/7779522 Knowledge Graphs (KGs) or Knowledge Graph (KG) have emerged as a significant technology to store the semantics of the domain entities. KGs have been used in a variety of industries, including banking, the automobile industry, oil and gas, pharmaceutical and health care, publishing, media, etc. The data is mapped and populated to the RGOM classes and relationships using an automated solution based on JenaAPI, producing an I4.0 KG. It contains more than 2.5 million axioms and about 1 million instances. This KG enables us to demonstrate the adaptability and usefulness of the RGOM. Our research helps the production line staff to take timely decisions by exploiting the information embedded in the KG. In relation to this, the RGOM adaptability is demonstrated with the help of a use case scenario to discover required information such as current temperature at a particular time, the status of the motor, tools deployed on the machine, etc.

The growing complexity and interdisciplinary nature of Materials Science research demand efficient data management and exchange through structured knowledge representation. Domain-Level Ontologies (DLOs) for Materials Science have emerged as a valuable tool for describing materials properties, processes, and structures, enabling effective data integration, interoperability, and knowledge discovery. However, the harmonization of DLOs, and, more generally, the establishment of fully interoperable multi-level ecosystems, remains a challenge due to various factors, including the diverse landscape of existing ontologies. This work provides, for the first time in literature, a comprehensive overview of the state-of-the-art of DLOs for Materials Science, reviewing more than 40 DLOs and highlighting their main features and purposes. Furthermore, an alignment methodology including both manual and automated steps, making use of Top-Level Ontologies’ (TLO) capability of promoting interoperability, and revolving around the engineering of FAIR standalone entities acting as minimal data pipelines (“bridge concepts”), is presented. A proof of concept is also provided. The primary aspiration of this undertaking is to make a meaningful contribution towards the establishment of a unified ontology framework for Materials Science, facilitating more effective data integration and fostering interoperability across Materials Science subdomains.

Objectives RA is a chronic disabling disease affecting 0.5-1% of adults worldwide. People with RA have a greater prevalence of multimorbidity, particularly osteoporosis and associated fractures. Recent studies suggest that fracture risk is related to both non-RA and RA factors, whose importance is heterogeneous across studies. This study seeks to compare baseline demographic and DXA data across three cohorts: healthy controls, RA patients and a non-RA cohort with major risk factors and/or prior major osteoporotic fracture (MOF).Methods This is a cross-sectional study using data collected from three DXA centres in the west of Ireland from January 2000 to November 2018.Results Data were available for 30 503 subjects who met our inclusion criteria: 9539 (31.3%) healthy controls, 1797 (5.9%) with RA and 19 167 (62.8%) others. Although age, BMI and BMD were similar between healthy controls, the RA cohort and the other cohort, 289 (16.1%) RA patients and 5419 (28.3%) of the non-RA cohort had prior MOF. In the RA and non-RA cohorts, patients with previous MOF were significantly older and had significantly lower BMD at the femoral neck, total hip and spine.Conclusion Although age, BMI and BMD were similar between a healthy control cohort and RA patients and others with major fracture risk factors, those with a previous MOF were older and had significantly lower BMD at all three measured skeletal sites. Further studies are needed to address the importance of these and other factors for identifying those RA patients most likely to experience fractures. What does this mean for patients?Rheumatoid arthritis (RA) is a disabling disease affecting millions of people worldwide. This disease causes pain, disability and other problems. RA affects not only joints (e.g. hip, knee, wrist), but also the bones, lungs, eyes and other body tissues. International studies show that people with RA are almost three times as likely to break a bone as the general population. Experts conclude that this is because of bone loss from the inflammation in RA. We measure bone mineral density (BMD) to manage osteoporosis in clinical practice with a test known as a DXA scan. People with lower BMD are more likely to break bones, causing further suffering and illness. In this study, we found that Irish patients with RA are much more likely to have fractures. An especially interesting finding in our study is that although the RA patients had similar age and BMD to healthy controls, far more of them had fractures. However, those with broken bones had lower BMD than those without, whether they had RA or not. Our findings suggest that more research is needed to gain a better understanding of why RA patients are prone to fractures, in order to prevent fracture occurrence in future.

ABSTRACT Osteoporosis is a common disease that has a significant impact on patients, healthcare systems, and society. World Health Organization (WHO) diagnostic criteria for postmenopausal women were established in 1994 to diagnose low bone mass (osteopenia) and osteoporosis using dual‐energy X‐ray absorptiometry (DXA)‐measured bone mineral density (BMD) to help understand the epidemiology of osteoporosis, and identify those at risk for fracture. These criteria may also apply to men ≥50 years, perimenopausal women, and people of different ethnicity. The DXA Health Informatics Prediction (HIP) project is an established convenience cohort of more than 36,000 patients who had a DXA scan to explore the epidemiology of osteoporosis and its management in the Republic of Ireland where the prevalence of osteoporosis remains unknown. In this article we compare the prevalence of a DXA classification low bone mass (T‐score < −1.0) and of osteoporosis (T‐score ≤ −2.5) among adults aged ≥40 years without major risk factors or fractures, with one or more major risk factors, and with one or more major osteoporotic fractures. A total of 33,344 subjects met our study inclusion criteria, including 28,933 (86.8%) women; 9362 had no fractures or major risk factors, 14,932 had one or more major clinical risk factors, and 9050 had one or more major osteoporotic fractures. The prevalence of low bone mass and osteoporosis increased significantly with age overall. The prevalence of low bone mass and osteoporosis was significantly greater among men and women with major osteoporotic fractures than healthy controls or those with clinical risk factors. Applying our results to the national population census figure of 5,123,536 in 2022 we estimate between 1,039,348 and 1,240,807 men and women aged ≥50 years have low bone mass, whereas between 308,474 and 498,104 have osteoporosis. These data are important for the diagnosis of osteoporosis in clinical practice, and national policy to reduce the illness burden of osteoporosis. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Summary Appropriate use of FRAX reduces the number of people requiring DXA scans, while contemporaneously determining those most at risk. We compared the results of FRAX with and without inclusion of BMD. It suggests clinicians to carefully consider the importance of BMD inclusion in fracture risk estimation or interpretation in individual patients. Purpose FRAX is a widely accepted tool to estimate the 10-year risk of hip and major osteoporotic fracture in adults. Prior calibration studies suggest this works similarly with or without the inclusion of bone mineral density (BMD). The purpose of the study is to compare within-subject differences between FRAX estimations derived using DXA and Web software with and without the inclusion of BMD. Method A convenience cohort was used for this cross-sectional study, consisting of 1254 men and women aged between 40 and 90 years who had a DXA scan and complete validated data available for analysis. FRAX 10-year estimations for hip and major osteoporotic fracture were calculated using DXA software (DXA-FRAX) and the Web tool (Web-FRAX), with and without BMD. Agreements between estimates within each individual subject were examined using Bland–Altman plots. We performed exploratory analyses of the characteristics of those with very discordant results. Results Overall median DXA-FRAX and Web-FRAX 10-year hip and major osteoporotic fracture risk estimations which include BMD are very similar: 2.9% vs . 2.8% and 11.0% vs . 11% respectively. However, both are significantly lower than those obtained without BMD: 4.9% and 14% respectively, P < 0.001. Within-subject differences between hip fracture estimates with and without BMD were < 3% in 57% of cases, between 3 and 6% in 19% of cases, and > 6% in 24% of cases, while for major osteoporotic fractures such differences are < 10% in 82% of cases, between 10 and 20% in 15% of cases, and > 20% in 3% of cases. Conclusions Although there is excellent agreement between the Web-FRAX and DXA-FRAX tools when BMD is incorporated, sometimes there are very large differences for individuals between results obtained with and without BMD. Clinicians should carefully consider the importance of BMD inclusion in FRAX estimations when assessing individual patients.

Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.