Welcome to the RWT Staff Publications Repository
The repository contains the records of published and unpublished research authored by NHS staff working for the Royal Wolverhampton NHS Trust and its partners. The repository is managed by the Library and Knowledge Services of the Trust and supported by the Non-Medical Research Leads Network Group and the Research and Development Directorate.
If you are a member of RWT staff and you would like to submit an item to the repository, please fill in this online form.
If you have a list of publications you'd like to submit, please e-mail the repository rwh-tr.rwtrepository@nhs.net admin team.
For more information contact the library on 01902 695322 or email or take a look at our website. You will also find guidance on the webpage about publishing your work.
Recent Submissions
Item British Nuclear Medicine Society SeHCAT guidelines.(Wolter Kluwer, 2024-07-01)Item Radiomics in oncology and radiology: statistical significance versus clinical significance.(Elsevier, 2024-09-01)No abstract availableItem Arrhythmogenic cardiomyopathy: current updates and future challenges.(IRM Press, 2024-07-04)Arrhythmogenic cardiomyopathy (ACM) epitomises a genetic anomaly hallmarked by a relentless fibro-fatty transmogrification of cardiac myocytes. Initially typified as a right ventricular-centric disease, contemporary observations elucidate a frequent occurrence of biventricular and left-dominant presentations. The diagnostic labyrinth of ACM emerges from its clinical and imaging properties, often indistinguishable from other cardiomyopathies. Precision in diagnosis, however, is paramount and unlocks the potential for early therapeutic interventions and vital cascade screening for at-risk individuals. Adherence to the criteria established by the 2010 task force remains the cornerstone of ACM diagnosis, demanding a multifaceted assessment incorporating electrophysiological, imaging, genetic, and histological data. Reflecting the evolution of our understanding, these criteria have undergone several revisions to encapsulate the expanding spectrum of ACM phenotypes. This review seeks to crystallise the genetic foundation of ACM, delineate its clinical and radiographic manifestations, and offer an analytical perspective on the current diagnostic criteria. By synthesising these elements, we aim to furnish practitioners with a strategic, evidence-based algorithm to accurately diagnose ACM, thereby optimising patient management and mitigating the intricate challenges of this multifaceted disorder.Item Digital health and self-management in idiopathic inflammatory myopathies: a missed opportunity?(Springer Link, 2024-08-08)Purpose of Review: This paper explored the potential of digital health in idiopathic inflammatory myopathies (IIMs), with a focus on self-management. Digital self-management technology includes tailored treatment plans, symptom tracking, educational resources, enhanced communication, and support for long-term planning. Recent Findings: After arguing the importance of digital health in IIMs management, from diagnosis until treatment, our literature review revealed a notable gap in research focusing on the efficacy of digital self-management interventions for individuals with IIMs, with no randomised controlled trials or observational studies addressing this topic. Summary: Our review further highlighted the significant unmet need for research in self-management interventions for individuals with IIMs. The absence of studies underscores the necessity for collaborative efforts to address this gap and develop personalised, effective strategies for managing IIMs using digital technology. Individuals with IIMs deserve tailored self-management approaches akin to those available for other rheumatic and musculoskeletal diseases.Item Machine learning for patient-based real-time quality control (PBRTQC), analytical and preanalytical error detection in clinical laboratory.(PMC PubMed Central, 2024-08-20)The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.
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