Background: The priorities for UK emergency medicine research were defined in 2017 by a priority setting partnership coordinated by the Royal College of Emergency Medicine in collaboration with the James Lind Alliance (JLA). Much has changed in the last 5 years, not least a global infectious disease pandemic and a significant worsening of the crisis in the urgent and emergency care system. Our aim was to review and refresh the emergency medicine research priorities.
Methods: A steering group including patients, carers and healthcare professionals was established to agree to the methodology of the refresh. An independent adviser from the JLA chaired the steering group. The scope was adult patients in the ED. New questions were invited via an open call using multiple communications methods ensuring that patients, carers and healthcare professionals had the opportunity to contribute. Questions underwent minisystematic (BestBETs) review to determine if the question had been answered, and the original 2017 priorities were reviewed. Any questions that remained unanswered were included in an interim prioritisation survey, which was distributed to patients, carers and healthcare professionals. Rankings from this survey were reviewed by the steering group and a shortlist of questions put forward to the final workshop, which was held to discuss and rank the research questions in order of priority.
Results: 77 new questions were submitted, of which 58 underwent mini-systematic review. After this process, 49 questions (of which 32 were new, 11 were related to original priorities and 6 unanswered original priorities were carried forward) were reviewed by the steering group and included in an interim prioritisation survey. The interim prioritisation survey attracted 276 individual responses. 26 questions were shortlisted for discussion at the final prioritisation workshop, where the top 10 research priorities were agreed.
Conclusion: We have redefined the priorities for emergency medicine research in the UK using robust and established methodology, which will inform the agenda for the coming years.
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http://dx.doi.org/10.1136/emermed-2022-213019 | DOI Listing |
Ann Intern Med
January 2025
Center for Healthcare Delivery Sciences, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts (R.J.D., N.K.C., N.H., J.C.L.).
Background: The evidence informing the harms of gabapentin use are at risk of bias from comparing users with nonusers.
Objective: To describe the risk for fall-related outcomes in older adults starting treatment with gabapentin versus duloxetine.
Design: New user, active comparator study using a target trial emulation framework.
Pediatr Emerg Care
January 2025
University of California Davis School of Medicine, Sacramento, CA.
Objective: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric emergency department patients and assess the impact of medically oriented fine-tuning.
View Article and Find Full Text PDFJ Neurosurg Case Lessons
January 2025
Department of Neurosurgery, Hirosaki University Graduate School of Medicine, Hirosaki, Aomori, Japan.
Background: Cases of congenital disorders of glycosylation (CDGs) are rare, and the occurrence of hemorrhagic infarction is also rare. The etiology is unclear.
Observations: A 3-year-old Asian boy with CDG type 1A was hospitalized with pneumonia.
J Med Internet Res
January 2025
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.
Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.
Crit Care Med
January 2025
Department of Intensive Care, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.
Objectives: Randomized clinical trials informing clinical practice (e.g., like large, pragmatic, and late-phase trials) should ideally mostly use harmonized outcomes that are important to patients, family members, clinicians, and researchers.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!