Introduction: Adverse drug reactions (ADRs) can have significant negative impact on peoples' daily lives, with physical, economic, social and/or psychological effects. Patient reporting of ADRs has been facilitated by pharmacovigilance systems across Europe. However, capturing data on patients' experiences of ADRs has proved challenging. Existing patient reports to the UK Yellow Card Scheme contain free-text comments which could be useful sources of information.
Objectives: To investigate patients' experiences of ADRs and their impact on patients as described in free-text data within patient Yellow Card (YC) reports submitted to the Medicines and Healthcare products Regulatory Agency.
Methods: A qualitative review of narrative texts was conducted on free-text data from 2255 patient YC reports from July to December 2015.
Results: Three key narrative themes emerged from analysis of the free-text data in 2255 reports: (1) identification of ADRs, (2) severity and impact of ADRs, and (3) management of ADRs. Temporal associations were the most common method of identification followed by differential diagnoses and confirmation with information sources such as healthcare professionals (HCPs). A combination of explicit and implicit impacts were described: physical, psychological, economic and social effects often persisted and caused serious disruption to many patients' lives. A range of strategies were used to manage ADRs, including consultation with HCPs, stopping/reducing the medicine or taking medicines to alleviate symptoms.
Conclusion: Free-text data from YC reports has been an underutilised resource to date, but this research has confirmed its potential value to pharmacovigilance and medication safety research.
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http://dx.doi.org/10.1111/bcp.15263 | DOI Listing |
Introduction: Individual case reports are essential to identify and assess previously unknown adverse effects of medicines. On these reports, information on adverse events (AEs) and drugs are encoded in hierarchical terminologies. Encoding differences may hinder the retrieval and analysis of clinically related reports relevant to a topic of interest.
View Article and Find Full Text PDFJ Gen Intern Med
January 2025
Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.
Background: COVID-19 increased the burden of childcare on parents, leaving women vulnerable to increased disparities in the division of domestic labor. Women healthcare workers may be at heightened risk of worsening gender parity in the workplace as a result.
Objective: To examine the impact of the COVID-19 pandemic on gender parity in the division of household responsibilities among women healthcare workers.
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
Seizure
January 2025
Peninsula School of medicine, University of Plymouth, Truro, United Kingdom; The Royal Wolverhampton NHS Trust, Wolverhampton, United Kingdom. Electronic address:
Background: Epilepsy is one of the commonest neurological conditions worldwide and confers a significant mortality risk, partly driven by status epilepticus (SE). Terminating SE is the goal of pharmaceutical rescue therapies. This survey evaluates UK-based healthcare professionals' clinical practice and experience in community-based rescue therapy prescribing.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Background Large-scale secondary use of clinical databases requires automated tools for retrospective extraction of structured content from free-text radiology reports. Purpose To share data and insights on the application of privacy-preserving open-weights large language models (LLMs) for reporting content extraction with comparison to standard rule-based systems and the closed-weights LLMs from OpenAI. Materials and Methods In this retrospective exploratory study conducted between May 2024 and September 2024, zero-shot prompting of 17 open-weights LLMs was preformed.
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