Background: The aim of this study is to externally validate the "Deformity, Edema, and Pain in Pronation" model, which determines the necessity for radiography in patients with wrist trauma.
Methods: This prospective, cross-sectional study was performed in a tertiary emergency department (ED). All patients admitted to the ED with wrist trauma aged 18 years and older were included in the study. Patients who did not have acute and blunt wrist trauma, those who could not be fully examined due to various reasons, and patients who did not wish to participate were excluded. Each patient was examined by their responsible physician, and imaging tests were requested based on the indications. All radiographic images were evaluated by an orthopedic surgeon who was blinded to the clinical information. This evaluation was accepted as the standard reference for diagnosing fractures.
Results: 391 patients were included in the study. 170 patients (43.5%) had at least one fracture. The sensitivity and specificity of the model formed in our study in predicting wrist fractures were 98.77% (95% CI: 95.61-99.85) and 27.60% (95% CI 21.82-34.00), respectively. The area under the receiver operating characteristic curve (AUC) on external validation of the model was 0.878 (p < 0.001; 95% CI: 0.844-0.913). With the practice of this rule, there would be a 16% decrease in X-ray imaging request, while only 0.5% patients would have missed inoperable fractures.
Conclusion: The "deformity, edema, and pain in pronation" model is a reliable and practical clinical decision rule for determining the necessity of radiography in wrist trauma.
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http://dx.doi.org/10.1016/j.ajem.2024.01.011 | DOI Listing |
Lung Cancer
January 2025
Dept. of Medical Oncology, Princess Margaret Cancer Center, Toronto, ON, Canada.
Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.
View Article and Find Full Text PDFAnn Afr Med
December 2024
Department of Public Health Dentistry, Government Dental College, Kottayam, Kerala, India.
Introduction: In recent years, patient preferences and attitudes have become crucial in shaping dental treatment choices and service utilization. Understanding these preferences is crucial for improving service delivery and patient satisfaction.
Aim: This study aims to comprehensively analyze the factors influencing these preferences, focusing on demographic, socio-economic, and behavioral variables, and the growing role of social media in healthcare decisions.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.
Methods: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study.
JMIR Perioper Med
January 2025
Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States.
Background: Postoperative delirium (POD) is a common complication after major surgery and is associated with poor outcomes in older adults. Early identification of patients at high risk of POD can enable targeted prevention efforts. However, existing POD prediction models require inpatient data collected during the hospital stay, which delays predictions and limits scalability.
View Article and Find Full Text PDFTransl Vis Sci Technol
January 2025
Institute of the Electrical and Biomedical Engineering, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tyrol, Austria.
Purpose: To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline.
Methods: Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity.
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