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Large language models in oncology: promise, pitfalls, and the path to real-world adoption Sam McInerney et al Cancer

Oncology services across the UK and Europe are under significant pressure. Cancer diagnoses are projected to increase by 21% by 2040. With a system already at breaking point, the need for tools to support safe, evidence-based decision making has never been greater.
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Hospital ward transitions and outcomes in critical care survivors: A multi-hospital cohort study Toby Falodun et al Emergency Care

Background: Hospital ward transitions remain poorly understood despite their importance to patient flow and efficiency. Transition patterns are shaped by local policy and pathways, but in critically ill patients, transitions may also accumulate in response to clinical complexity. This study aimed to characterise patterns of ward transitions, identify determinants of ward transition frequency, and evaluate associations with adverse patient outcomes.
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Natural language processing-driven knowledge graphs for transformative public health intelligence and research datasets in urgent and emergency care Chris Humphries et al Emergency Care

Urgent and emergency care generates large volumes of clinical data, yet much of the information most useful for research, surveillance, and service planning is recorded only in free-text clinical notes and never reaches the structured datasets used for analysis. As a result, the reasons patients present, the upstream pressures driving demand, and the care that was needed but unavailable remain largely invisible, with consequences that fall most heavily on people who move repeatedly across services.
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Geriatric Syndromes Extraction from Discharge Summaries: A New Dataset, Annotation Scheme and Initial Findings Imane Guellil et al Ageing and later life

Introduction: Geriatric syndromes (GS), such as falls, dementia, delirium and malnutrition, are complex clinical conditions affecting older adults which involve multiple organ systems and have major impact on quality of life and care. GS cut across disease categories, and are poorly represented in structured electronic health records. Natural language processing (NLP) offers an opportunity to extract valuable GS-related information from unstructured clinical text, such as hospital discharge summaries.
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Mental Health Outcomes Among Long-Term Survivors of Childhood, Adolescent and Young Adult Cancer: A Scottish Population-Based Cohort Study Emanuela Molinari et al Mental Health

Background: Survivors of cancer face increased risks of long-term morbidity compared to people without a cancer history. Aims: To quantify the long-term burden of mental health morbidity and to evaluate the healthcare settings in which these events are identified in long-term Scottish cancer survivors diagnosed before 40 years of age.
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Treatment resistant depression in electronic health records: definitions matter Matthew H Iveson et al Mental Health

Background: Many people with depression do not respond well to the first antidepressant prescribed. Treatment Resistant Depression (TRD) refers to depression which does not respond to multiple subsequent antidepressant treatments. Identifying TRD in routinely-collected health records is challenging due to limited response-related data. Previous studies have used definitions based on the number of antidepressant switches observed. However, these do not account for other features clinically indicative of treatment resistance, such as augmentation of antidepressants with lithium or ...
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Unlocking clinical narratives: how natural language processing and artificial intelligence can address data deficits and mitigate health inequities in urgent and emergency care Chris Humphries et al Emergency Care

The Urgent and Emergency Care system generates a wealth of clinical information, but our ability to harness this for public health planning and to address health inequalities is constrained by systemic data quality issues. Modern natural language processing (NLP), driven by the context-aware capabilities of transformer-based architectures and large language models, offers a transformative opportunity to bridge this gap. By training machines to interpret and structure context-rich clinical notes at scale, we can translate complex patient stories into data ready for research and sy...
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Comorbidities, medication use, and overall survival in eight cancers: a multinational cohort study of 1.7 million patients across Europe Irene López-Sánchez et al Cancer

Background: Real-world evidence provides valuable insights into cancer burden, presentation, and care variations. Through a large-scale federated approach, this study aims to explore patient characteristics and overall survival for eight cancers using data from 11 electronic health records and cancer registries from eight European countries, mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM).
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