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Understanding the emergency medical service response to patients who are at imminent risk of out-of-hospital cardiac arrest
Get PDFStudy Aim: To understand the survival and emergency medical service call triage and response to patients who suffer an out-of-hospital cardiac arrest after the emergency medical service call is connected. Methods: A descriptive retrospective data analysis of two years’ of out-of-hospital cardiac arrest registry data, 2018-2019, linked to computer aided dispatch data was completed in a single system emergency medical service in the South West of the United Kingdom. Data analysis included descriptive statistics and chi squared analysis for association. Results: There were 4245 out-of-hospital cardiac arrest patients included for analysis. Patients who suffer an out-of-hospital cardiac arrest after the emergency call is connected receive a lower priority response, have a significantly longer response time and where the cardiac arrest is not witnessed by the emergency medical service have poorer outcomes than those patients who have already suffered a cardiac arrest at the time of the emergency call. Conclusion: Emergency medical service response times were significantly shorter for patients who had already suffered a cardiac arrest at the time of the call for help in comparison to those patients who continued to suffer a cardiac arrest after the call for help was connected. Patients who suffer a cardiac arrest after the connection of the emergency call and where the out-of-hospital cardiac arrest is not emergency medical service witnessed have the poorest outcomes
Using Machine Learning (ML) for Heat Transfer Coefficient (HTC) measurement in buildings: A systematic review
Get PDFAccurate, fast, and non-intrusive Heat Transfer Coefficient (HTC) estimations of existing buildings are crucial for informed decision-making in retrofit projects. A systematic integrative literature review was conducted following the PRISMA standard (1997–2024). This is the first review with statistical and critical thematic analysis on this topic, synthesizing a theoretical framework for future research directions. The review identified 63 relevant sources.Quantitative analysis reveals exponential growth of publications since 2017 on ML for HTC estimation, with leadership from China, USA and India. Qualitative analysis assessed existing ML methodologies, data collection protocols, and validation frameworks. Indoor and outdoor temperatures emerge as the most frequently used parameters; 45 % of studies use hourly measurement intervals. High-frequency data collection offers superior capture of thermal dynamics but poses challenges in data storage and processing. Neural network-based approaches (particularly multilayer perceptrons (MLP) and long short-term memory (LSTM)) dominate to capture non-linear thermal relationships. Key challenges include data quality requirements, computational efficiency, and the trade-off between model complexity and interpretability. A concerning finding shows 25.7 % of studies lack validation against established methods, highlighting a critical gap in standardization.This analysis provides insights into current methodological trends, identifies research gaps, and proposes future directions for advancing ML applications in this field. Findings emphasize the need for standardized validation protocols, improved data collection strategies, and integrated approaches connecting component-level and whole-building analyses
Editorial: Deciphering signaling pathway interactions in tissue homeostasis
No full textCells, tissues and organisms need to maintain levels and balances of metabolites and functional systems whilst constantly being under the influence of external pressures, a process known as homeostasis. Cells need to achieve this to survive and prosper, and such control is determined by the cell signaling pathways involved. There have been many reviews on the topic, for example, showing the range of cell types and tissues that have been studied, such as B cells (Woodland et al., 2006), T cells (Sprent et al. 2008), and liver cells (Stanger, 2015). To maintain cellular homeostasis, cells need to perceive a range of extracellular signals, and coordinate these into an appropriate response. This involves extracellular signals such as cytokines, oxygen levels and ATP, as well as cell surface receptors, intracellular signaling pathways and intracellular organelles, in a process which has been dubbed the homeostatic circuit (Meizlish et al. 2021). This is an extremely complex, but instrumental system, to ensure cellular, and organismal, longevity
Development and clinical interpretation of an explainable AI model for predicting patient pathways in the emergency department: A retrospective study
Get PDFBackground: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH’s EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN’s AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow
High-fidelity simulation in healthcare education: Design and delivery considerations for optimising teaching and learning in higher education
No full textIntroduction: Simulation-based learning (SBL) is a recognised teaching and learning tool within higher education (HE) and one capable of facilitating skill retention and knowledge retrieval. Successfully achieving these outcomes relies on effective design, delivery and debriefing; yet a limited range of publications draw together these fundamental components. High-fidelity simulation (HFS) describes a sub-division of SBL that, in recent years, has generated traction within healthcare education.Aims: To support educators in orchestrating HFS with greater impact and influence, the author set out to compose an article outlining five constructs that collectively possess scope to optimise HE teaching and learning outcomes. These five constructs consist of: (1) creating a believable scenario; (2) integrating the five principles of ‘fidelity harmony’; (3) selecting an appropriate modality; (4) adopting a clear pedagogical stance; and (5) amalgamating concepts of experiential learning theory into the briefing and debriefing. When dynamically incorporated, important gaps between theory and practice can be bridged and learner experience will be significantly enhanced.Conclusion: This article offers HE educators a series of recommendations for creating deeply immersive learning experiences for augmenting learner performance, and provides a new definition for HFS, which challenges the erroneous notion that ‘high fidelity’ represents ‘high technology’
Apples and oranges? The importance of recognising heterogeneity in Arts and Health research publications
Get PDFThis opinion text seeks to advance the understanding of arts-based interventions in public mental health by moving beyond generalised claims that “the arts are good for health.” Just as the term "medicine" requires specificity to assess efficacy, so too must we detail the nature of the artistic activities and facilitation involved in arts for health research. By treating all art forms and delivery methods as interchangeable, we risk drawing misleading conclusions. This work aims to clarify the types, contexts, and mechanisms of arts activities used in order to better understand how and why they support mental wellbeing
Healthcare leaders and professionals’ perspectives of the ICON programme to prevent abusive head trauma in infants: A qualitative study
No full textBackground: Abusive head trauma (AHT) in infants is the most common abusive injury in young children, and increased awareness has resulted in the development of prevention programmes. Most research evaluating AHT prevention programmes report parental and carer perspectives. Little is known about barriers and facilitators to adopting, implementing, and maintaining educational programmes from the perspectives of managers and staff delivering the education. ICON is an AHT prevention programme currently being delivered in National Health Service hospital and primary care settings in the United Kingdom. Methods: This study evaluated the ICON programme from the perspective of managers and healthcare professionals through the RE-AIM framework using qualitative methods. Fifty-three managers and healthcare professionals across six geographical areas in England participated in individual interviews and focus groups between October 2022 and April 2023. Data collection and analysis were concurrent, systematic, and iterative, using framework analysis as a guide to explore factors impacting ICON’s reach and the key enablers and obstacles to its effectiveness, adoption, implementation, and maintenance. Results: Four primary enablers and related challenges to the ICON programme’s impact were identified. Fidelity to the programme’s recommended touchpoints and message impacted ICON’s reach to new parents and carers. Parental receptiveness to the programme was affected by staff individualising their approach. Staff buy-in was related to staff workload and previous experiences with AHT. Managers with strategic leadership responsibility for reducing infant mortality and able to provide governance oversight fostered successful adoption, implementation, and maintenance of the programme. Conclusions: Staff are willing and able to deliver the ICON programme, including, where necessary, delivering the key messages in a format acceptable to families varying situations, if given the workload and training to do so. Those in leadership positions influence the likelihood of successful adoption, delivery and longer-term mainstreaming, if they are able to prioritise the programme. Understanding the barriers and facilitators to ICON’s delivery has the potential to inform policy by facilitating the uptake of the programme by settings, enabling delivery of ICON to reach the needs of local families, and ensuring sustainability of the ICON programme
A contemporary review of collaborative robotics employed in manufacturing finishing operations: Recent progress and future directions
Get PDFThe final phase of the manufacturing process for any artefact involves their surface finishing operations. This phase entails the precise removal of small volumes of material to achieve a specific surface roughness, which is essential for ensuring the artefact’s post-production performance and endurance. For certain tooling, such as molds and dies, the finishing operation can be particularly significant, often equating to fifty percent of the total production time and a fifth of the overall manufacturing cost. In recent years, collaborative robotics has come to the fore. These advanced systems allow manufacturers to harness the positive attributes of robots, such as their repeatability, endurance, and strength, while simultaneously leveraging the unique benefits of human workers, including their process knowledge, problem-solving abilities, and adaptability. This co-operation between human and robotic capabilities has opened new avenues for efficiency and precision in the finishing process. This paper investigates the current advancements in collaborative robotic finishing, providing a comprehensive overview of the latest technologies and methodologies. It also highlights existing research gaps that need to be addressed to further enhance the effectiveness of these systems. Additionally, the paper suggests potential areas for future investigation, aiming to drive continued innovation and improvement in the field of collaborative robotic finishing operations
Connectedness between sectoral cryptos and counterpart stocks
Get PDFThis study examines the interconnection between sectoral cryptocurrencies and their corresponding stocks across 14 industries from 2022-2024. Using wavelet coherence, we evaluate crypto-stock interconnectedness across multiple investment frequencies and timescales and identify cross-sector patterns using k-means clustering of coherence maps. We conduct two types of analyses: (1) a within-sector dynamics, assessing time-varying connectedness between each crypto-stock pair; and (2) cross-sector grouping to uncover common regimes. Results show weak, fragmented, and short-lived in cloud computing, telecommunications, gaming and gambling, with only sporadic bursts. In contrast, supply chain and education exhibit strong, persistent long-term coherence. Insurance, cybersecurity, and e-commerce display episodic, event-driven coherence, with peaks around policy shifts and major sector news. These findings highlight that crypto-stock connectedness is sector- and horizon-dependent rather than uniform. The evidence informs sector-specific risk management, portfolio construction, and timing of tokenisation strategies, and supports more tailored regulatory oversight
Magneto-agglutination biosensor system for rapid detection of bacteria causing urinary tract infections
Get PDFGlobally, urinary tract infection (UTI) is very common and a primary reason for antibiotic prescribing. Currently, standard testing procedures involving laboratory culture of samples are slow and often patients are treated on symptoms alone, risking inappropriate treatment when not required. In this work, a novel biosensing system based on a magneto-agglutination assay is developed and evaluated. The system comprises an easy-to-use, multianalyte, urinalysis cartridge, and an instrument, which incorporates a fluid flow control unit, precision magnet movement and sensor coils plus magnetometers for each channel. The cartridge incorporates a range of features to ensure sensitive and reproducible measurement, from tolerating high variability in the volume of sample introduced to an in-built quality control mechanism through dynamic data capture. Measurement sensitivity for E.coli, Proteus and Klebsiella was high, with concentrations of pathogens as low as 102 CFUs quantified. In a small study on 30 patient urine samples, high clinical sensitivity was indicated. All 3 assay measurements (from urine sample input to result output) were completed in 7 minutes, making the magneto-agglutination biosensor system suitable for point-of-care applications