Leeds Teaching Hospitals NHS Trust is an NHS hospital trust in Leeds, West Yorkshire, England.The Trust was formed in April 1998 after the merger of two previous smaller NHS trusts to form one citywide organisation. The former trusts were United Leeds Teaching Hospitals NHS Trust (based at Leeds General Infirmary) and St James's & Seacroft University Hospitals NHS Trust (based at St James's University Hospital). The Trust has an overall income of around £1 billion and provides local and specialist services for the immediate population of 770,000 and regional specialist care for up to 5.4 million people.The Trust is rated as Good by the Care Quality Commission.
The present guideline summarizes all aspects of patch testing for the diagnosis of contact allergy in patients suspected of suffering, or having been suffering, from allergic contact dermatitis or other delayed-type hypersensitivity skin and mucosal conditions. Sections with brief descriptions and discussions of different pertinent topics are followed by a highlighted short practical recommendation. Topics comprise, after an introduction with important definitions, materials, technique, modifications of epicutaneous testing, individual factors influencing the patch test outcome or necessitating special considerations, children, patients with occupational contact dermatitis and drug eruptions as special groups, patch testing of materials brought in by the patient, adverse effects of patch testing, and the final evaluation and patient counselling based on this judgement. Finally, short reference is made to aspects of (continuing) medical education and to electronic collection of data for epidemiological surveillance.
Large language models (LLMs) offer promising tools for patient education, yet fixed knowledge cutoffs and hallucination risk limit their clinical utility. Current retrieval-augmented generation (RAG) approaches fail to distinguish between stable clinical knowledge and evolving recommendations. We developed and evaluated bRAGgen, a temporally anchored RAG framework incorporating five modules to enforce clinical protocols for MBS patient education: a semantic knowledge cache, multi-source evidence retrieval with graph-based fusion, uncertainty-aware generation, clinical constraint reranking, and Temporal Fisher Anchoring with Mechanism Selectivity (TFAMS) for adaptive inference. The framework was evaluated using 105 expert-curated free-response questions assessed by a multinational panel of seven specialists (5 surgeons, 2 dietitians) from five countries on a 5-point Likert scale for factuality, clinical relevance, and comprehensiveness. LLM-as-Judge evaluation using ChatGPT-4o provided complementary automated assessment. bRAGgen significantly improved response quality across all five base language models tested (p < 0.001), with large effect sizes for higher-capacity models (Cohen’s d = 0.96–1.01) and moderate effects for smaller models (Cohen’s d = 0.38–0.56) with good inter-rater reliability (Krippendorff’s α = 0.72). The largest gains occurred in safety-critical categories including Risks and Complications (+ 1.84 points) and Mental and Emotional Health (+ 1.84 points), suggesting the framework is most impactful where nuanced clinical judgment is essential. LLM-as-Judge evaluation using ChatGPT-4o demonstrated high concordance with expert ratings (Spearman’s ρ = 0.94). This proof-of-concept study suggests that a multi-module RAG framework with temporal stability anchoring can improve expert-rated LLM response quality for bariatric surgery domain knowledge, though prospective validation in patient-facing settings is needed before clinical implementation.
INTRODUCTION:The IBD UK Benchmarking surveys, conducted in 2019 and 2023, collected repeated data regarding the quality of inflammatory bowel disease (IBD) care across the UK using both service self-assessments and patient-reported experience measures (PREMs). We aimed to assess variation between patient and provider perspectives. METHODS:All UK hospitals offering specialist IBD services were invited to complete online surveys. Patients were invited through social media, charities, and clinical services. This study compared changes over the 4 years and examined alignment between healthcare-reported and patient-reported assessments. RESULTS:From 26 760 patient responses and 154 service assessments, patient-perceived care quality (PPCQ) declined between 2019 and 2023 (P < .001). Male sex and older age were associated with higher PPCQ. Greater disease severity was associated with lower PPCQ (P < .001). More patients reported IBD symptoms to impact activities of daily living in 2023 (P < .001). Factors associated with higher PPCQ included rapid diagnosis, being supported by an IBD team, and having knowledgeable IBD nurses. Access, information, communication, and empowerment were identified by patients as needing improvement (P < .001). Services with lowest quartile quality scores in 2019 demonstrated significant improvement over time, whilst those with highest 2019 scores demonstrated significant deterioration in PPCQ (P < .001). Services reported better performance than patients (P < .001). CONCLUSIONS:These data underscore the importance of assessing lived experience and the care quality perception gap between patients and service providers. Regular benchmarking including PREMs should be used to drive and assess service-level, national and international quality improvement initiatives.
Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. However, concerns remain about potential biases related to skin color, which can impact diagnostic outcomes. Ensuring fairness is challenging due to difficulties in classifying skin tones, high computational demands, and the complexity of objectively verifying fairness, given the continuous and context-dependent nature of skin tone and the dependence of fairness conclusions on metric choice and subgroup representation. To address these challenges, we propose a fairness algorithm for skin lesion classification that overcomes the challenges associated with achieving diagnostic fairness across varying skin tones. By calculating the skewness of the feature map in the convolution layer of the Visual Geometry Group network (VGG) and the patches and the heads of the Vision Transformer (ViT), our method reduces unnecessary channels related to skin tone, focusing instead on the lesion area. Application on VGG11 and ViT-B16, showed improved fairness metrics by 15-20% on average while maintaining accuracy and F1-score within 0.01 of the baseline. Additionally, the method reduced model size by 16% for VGG11 and decreased memory footprint for ViT-B16, without requiring skin tone labels at inference. Thus, the approach lowers computational costs and mitigates bias without relying on conventional statistical methods. It potentially reduces model size while maintaining fairness, making it more practical for real-world applications.
Background Fatigue, impaired sleep quality and daytime sleepiness, common in neurodegenerative and immune-mediated diseases, are debilitating and have serious societal and economic implications. Currently, measurement of these symptoms largely relies on self-reported questionnaires, which are burdensome for patients and lack sensitivity, granularity and reliability. Methods Building on a preceding feasibility study and qualification advice of the European Medicines Agency, the Clinical Observational Study of the European project Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities of daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases (IDEA-FAST) investigates the relationship between digital and clinical parameters of the target concepts of fatigue, reduced sleep quality and daytime sleepiness. Results Between 2022 and 2025, 2000 people are being recruited at 24 European sites – 500 with Parkinson's disease, 500 with inflammatory bowel disease, 200 with each of the following diseases: Huntington's disease, rheumatoid arthritis, systemic lupus erythematosus, primary Sjögren's syndrome and 200 healthy volunteers. Participants are followed over a 24-week period with four visits, each including a 1-week assessment phase at home using CE-certified digital health (including active and passive) technologies. The latter collect information on physical activity, physiology, cognition as well as social interaction and behaviour as core dimensions of the target concepts. Conclusion This study will help to develop reliable, valid and efficient digital endpoints of fatigue, impaired sleep quality and daytime sleepiness for use in future clinical studies and trials.
