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Cortrak feeding tube safety: Criteria for interpreting lung misplacement
No full textPneumothorax occurs in 0.52% of blind tube placements, with 97% occurring in-procedure. Post-procedure pH or x-ray checks cannot prevent these, but CO checks or guided tube placement can. Cortrak guided tube placement is widespread, but manufacturer guidance to interpret lung placement is subjective. Develop objective criteria to differentiate lung from oesophageal tube placement from measurements and patterns in Cortrak traces. Paired comparison of lung and oesophageal Cortrak traces using a retrospective analysis of prospectively collected data in critically ill patients. From 126 paired traces, lung position, versus oesophageal, was indicated by deviation from the sagittal midline further from the receiver and by a greater angle and distance. No lung trace moved deep to shallow and returned to the midline then turned left compared with 99.2% of oesophageal traces; 56.3% of traces had some degree of artefact caused by receiver misalignment and required interpretation to account for this. Differences in trace measurements give early warning of lung placement, and absence of an oesophageal pattern is definitive. Manufacturer guidance describing Cortrak trace is subjective, lacking advice on how to interpret or correct for artefacts. This could fail to prompt a 'lung warning' and/or lead to unnecessary withdrawal of oesophageal placements; both risk trauma. The objective criteria developed enable detection of lung placement. If regulatory authorities mandate their use in independently accredited training, Cortrak would be a safe method to confirm tube position. [Abstract copyright: © 2025 British Association of Critical Care Nurses.
Patient-reported outcomes 3 and 18 months after mastectomy and immediate prepectoral implant-based breast reconstruction in the UK Pre-BRA prospective multicentre cohort study
Get PDFIntroduction Prepectoral techniques are becoming standard of care for implant-based breast reconstruction due to reduced impact on chest wall function and improved patient satisfaction. Evidence to support these benefits, however, is lacking. Here, patient-reported outcomes (PROs) of prepectoral breast reconstruction (PPBR) in the Pre-BRA cohort are reported. Methods Women undergoing PPBR after mastectomy for breast cancer or risk reduction between July 2019 and December 2020 were recruited. Participants completed the BREAST-Q preoperatively and at 3 and 18 months following surgery together with a single item evaluating overall satisfaction at 18 months. Women completing at least one BREAST-Q scale at any timepoint were eligible for inclusion. Questionnaires were scored according to the developers’ instructions and scores compared over time. Exploratory analysis, adjusting for baseline scores was performed to explore factors impacting PROs. Results In total 338 of 343 (98.5%) women undergoing PPBR at 40 UK centres were included in the analysis. Compared with baseline scores, women reported statistically significant and clinically meaningful decreases in both ‘Physical’ and ‘Sexual well-being’ at 3 and 18 months. Adjusting for baseline, at 18 months, those experiencing implant loss or having surgery for malignancy reported lower scores in all BREAST-Q domains. Overall, two-thirds of women (167/251) rated the outcome of their reconstruction as ‘excellent/very good’, but experiencing major complications, implant loss, and being dissatisfied with wrinkling/rippling in the reconstructed breast were associated with reduced satisfaction. Conclusions PPBR impacts postoperative physical well-being and PROs are variable. These findings should be discussed with patients to support informed decision-making based on realistic expectations of outcome. Study registration ISRCTN11898000
Women, Organizations and Vulnerability: Global Archetypes
No full textWhy are women, despite being resilient, adaptable, and persistent, often constructed and perceived as weak and vulnerable? Women’s vulnerability is not a neutral concept but is organizationally defined and understood. Organizations are discursive spaces where women’s vulnerability is constructed and reproduced as a communicative act and event. We often represent vulnerability at individual or organizational levels, but not both. Women’s vulnerability reminds us of the pervasive interconnectedness of personal and organizational life events. Experiencing women’s organizational vulnerability is common. However, is women’s vulnerability publicly represented, defined, felt and acted upon in the same way everywhere?This book is focused on comparing women’s organizational vulnerability practices making a significant contribution to reflection, theory, methods and cross-disciplinary expertise. The process of making sense of “vulnerability” is extremely diverse and intersectionally constructed through gender, culture and organizational discourses, which demands complex, innovative and non-Eurocentric methodological paradigms and approaches. This book satisfies these demands by integrating contributions from a diverse range of disciplines, academic traditions and cases and provides an understanding of women’s vulnerability as a global phenomenon that comprises both cultural and organizational contexts.By examining how publicly and organizationally women develop particular and creative strategies to navigate vulnerability, the book significantly contributes towards identifying archetypical practices for negotiating vulnerability in different contexts
Devil in the details – Visual perception of the landscape features by potential residential buyers
No full textIt has long been established that people attach value to window views. However, the challenge in real estate market analyses is to capture what landscape features an attractive view contains and thus how they affect the worth (individual valuation) of the real estate. Real estate research predominantly uses questionnaires to analyze the perception of the landscape. This research assesses the possibilities of using eye-tracking as an objective tool for the assessment of the visual perception of the landscape. The research aim was achieved by comparing the results of subjective surveys with a qualitative analysis of the records of gaze patterns of participants observing on-screen photos of window views. All analyses concerned the urban landscape. Surveys show that natural areas are the most attractive for potential residential buyers, while the most undesirable are industrial window views. Participants of the eye-tracking study focused their attention on details such as distinctive buildings, construction machinery, road signs and traffic lights, advertisements, graffiti, murals, street lamps and electrical boxes. These undesirable details can obscure the entirety of even the most aesthetically pleasing landscape. Thus, the results of this study are expected to inform those involved in urban design to minimize the impact of such obstructions
Influence of proteinoids on calcium carbonate polymorphs precipitation in supersaturated solutions
Get PDFProteinoids, or thermal proteins, are amino acid polymers formed at high temperatures by non-biological processes. Pro- teinoids form microspheres in liquids. The microspheres exhibit electrical activity similar to that of neurons. The electrically spiking microspheres are seen as proto-neurons capable of forming networks and carrying out information transmission and processing. Previously, we demonstrated that ensembles of proteinoid microspheres can respond to optical and electrical stimulation, implement logical gates, recognise arbitrary wave forms, and undergo learning. Thus, the ensembles of proteinoid microspheres can be seen as proto-brains. In present paper we decided to uncover morphologies of these proto-brains. We utilise a supersaturated solution of calcium carbonate to facilitate the crystallisation of proteinoids and subsequently generate proteinoid brain structures. Our hypothesis suggests that calcium carbonate crystals have the potential to serve as scaffolds and connectors for proteinoid microspheres, thereby improving their electrical properties and facilitating communication. In this section, we outline the experimental methods and techniques used in our study. We share our findings and results regarding the morphology, composition, stability, and functionality of proteinoid brain structures. We discuss the implications and applications of our work in the fields of bio-inspired computing, artificial neural networks, and origin of life research
A decision-making model for public health authorities in circumstances of potentially high public risk
Get PDFBackgroundAn expert multidisciplinary panel was commissioned by a UK Health Security Agency led incident management team (IMT) to support decision making in the case of an individual with extensively drug-resistant tuberculosis. The behaviour and stated intentions of the individual were potentially a significant risk to public health, and the regional IMT felt unable to adequately balance the rights of the individual, versus the public health risk, within current processes and legal powers.MethodWe describe the composition, organization, implementation, and conclusions of a national, expert, multidisciplinary panel.ResultsThe national panel convened over three structured virtual meetings to consider the balance between the rights of the individual to an unrestricted life, and the duty to protect the public’s health. Evidence included briefs from the regional IMT and input from a public consultation group. Following the first two meetings the need for a literature review examining the success of surgical interventions was identified and conducted.ConclusionsEvidence and conclusions were mapped onto a custom-designed risk assessment template. The panel provided authoritative advice regarding the case, and developed a review methodology that is transferable to similar complex public health scenarios both in the UK and internationally
Acceptability and preferences of people with long-term conditions for delivery of digital healthcare interventions: Scoping review protocol
Get PDFBackground: Digital health interventions (DHIs) are prevalent and have been shown to help some people with long-term conditions (LTCs) to manage their condition. There are myriad options for digital delivery yet limited understanding of what modes of delivery are acceptable to people with LTCs. It is important to understand the acceptability of delivery methods of DHIs to inform future DHI development and promote engagement. This scoping review aims to explore the acceptability of the delivery of DHIs for people with LTCs. Methods and analysis: This review will follow the Joanna Briggs Institute guidance for scoping reviews and will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping reviews extension checklist. Databases including MEDLINE, PubMed, CINAHL, AHMED and PsycINFO will be searched for primary studies that provide data on preferences for delivery methods of DHIs by people with LTCs. Narrative analysis is anticipated, and a summary of the findings will be presented in a tabulated format. Ethics and dissemination: Ethical approval will not be required for this scoping review. The findings will be disseminated via appropriate peer-reviewed journals and conferences and PhD theses
Clinical AI scribes in primary care: Accuracy, error severity, and implications for clinical practice
Get PDFObjectives: To investigate the performance of commercially available Clinical AI Scribes (CAISs), assessing their accuracy, potential clinical impact of errors, and documentation quality, given growing concerns around errors and safety. Methods and analysis: Seven CAIS products were investigated, using eight standardised clinical consultation scenarios recorded as audio. CAIS-generated summaries were assessed against a human-validated transcript, and evaluated for errors (omissions, factual inaccuracies, and hallucinations). Error severity was rated by medical doctors, generating a novel severity-weighted Impact Score (linear and exponential variants), to quantify potential clinical impact. Further analysis using the Physician Documentation Quality Instrument (PDQI-10) (a validated clinical note quality score) reinforced the findings. Results: Omissions dominated error counts (83.8%, p<<0.001), with CAISs varying widely in error frequency and severity, and a median of 1-6 omissions per consultation (depending on CAIS). Although less frequent, hallucinations and factual inaccuracies were more often clinically serious. No tested CAIS produced error-free summaries. The Impact Score highlighted clinical severity, notably amplifying the significance of less frequent but high-severity errors. PDQI-10 analysis indicated summaries were weakest in succinctness and organisation, but strong in consistency and clinical usefulness. Conclusions: The CAISs demonstrate high levels of summarisation accuracy. However, there is great disparity between the currently available CAIS products and, whilst some perform well, none are perfect. Clinicians should therefore maintain vigilance, particularly checking omitted psychosocial details and medications, and scrutinising plausible-sounding insertions. Purchasers and regulators should be aware of the significant performance disparities identified, reinforcing the need for careful evaluation and selection of CAIS products
Empowering SMEs with SustainWater Bot to advance urban water sustainability
Get PDFClimate change, population growth, and resource constraints are intensifying pressure on urban water systems (UWSs), prompting a shift toward integrated information management. Due to their agility and reach, small- and medium-sized enterprises (SMEs) are central to this transition. However, many SMEs lack access to robust information systems (ISs) that consolidate government initiatives, industry trends, and broader water-related data, impeding sustainable adoption. This study introduces SustainWater Bot, a chatbot driven by generative artificial Intelligence (GenAI), including large language models (LLMs) and retrieval-augmented generation (RAG). Designed to fill this information gap, SustainWater Bot addresses the shortcomings of conventional LLMs, such as information misalignment, over-complexity, and information deficiencies. RAG enables semantic consolidation of various sources, such as news, government reports, industry insights, academic research, and social media, into an integrated IS. The evaluation results showed that RAG with LLM-based methods outperformed traditional information retrieval (IR) techniques, with Llama3.2:3b achieving top scores in precision (95 %), completeness (95 %), and exact match (90 %). Traditional IR techniques such as term frequency-inverse document frequency (TF-IDF) and best matching 25 (BM25) performed lower but offered quicker responses. SustainWater Bot supports informed decision-making through a question-answering (QA) framework that delivers relevant insights on sustainable urban water initiatives (SUWIs). It is built on open-source technologies and offers SMEs a cost-effective, scalable, and sustainable solution to enhance eco-friendly water practices and operational efficiency
A Stochastic Prototypical network for few-Shot intrusion detection in CAN-Based IoV network
Get PDFThe Controller Area Network (CAN) acts as the backbone of intra-vehicle communication in modern Internet of Vehicles (IoV) systems, enabling real-time coordination among critical automotive subsystems. Despite its widespread adoption, CAN lacks essential security mechanisms such as encryption and message authentication, rendering it highly vulnerable to cyberattacks that can jeopardize vehicle safety and operational integrity. Developing an effective Few-Shot Learning (FSL)-based Intrusion Detection System (IDS) for CAN networks presents challenges due to data scarcity, noisy traffic, dynamic attack patterns, and the need for real-time efficiency. Existing FSL approaches often rely on deterministic models that struggle to capture the uncertainty and variability inherent in CAN network traffic. To address these challenges, we propose a Stochastic Prototypical Network based on a Random Neural Network (RaNN) for few-shot intrusion detection in CAN-based networks. RaNNs are inherently stochastic, enabling them to model uncertainty and variability in network traffic. By integrating RaNN with the prototypical network, the proposed framework computes stochastic prototypes that represent the distribution of normal and attack behaviors, improving robustness in noisy and dynamic environments. Additionally, the framework quantifies uncertainty in its predictions, enabling the system to flag ambiguous cases for further analysis, thereby reducing the risk of both false positives and negatives. The proposed approach demonstrates high classification performance across all FSL scenarios, achieving a maximum accuracy of 99.17% in a 15-shot configuration. The framework shows impressive computational efficiency with millisecond inference times and minimal training overhead, making it suitable for real-time deployment