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Identification of Intracranial Germ Cell Tumors Based on Facial Photos: Exploratory Study on the Use of Deep Learning for Software Development.
J Med Internet Res
January 2025
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
View Article and Find Full Text PDFRisk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study.
J Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
Clozapine for Treatment-Resistant Disruptive Behaviors in Youths With Autism Spectrum Disorder Aged 10-17 Years: Protocol for an Open-Label Trial.
JMIR Res Protoc
January 2025
Graduate Program of Psychiatry and Behavioral Sciences, Department of Psychiatry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil.
Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition emerging in early childhood, characterized by core features such as sociocommunicative deficits and repetitive, rigid behaviors, interests, and activities. In addition to these, disruptive behaviors (DB), including aggression, self-injury, and severe tantrums, are frequently observed in pediatric patients with ASD. The atypical antipsychotics risperidone and aripiprazole, currently the only Food and Drug Administration-approved treatments for severe DB in patients with ASD, often encounter therapeutic failure or intolerance.
View Article and Find Full Text PDFAssessing the Adherence of ChatGPT Chatbots to Public Health Guidelines for Smoking Cessation: Content Analysis.
J Med Internet Res
January 2025
Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC, United States.
Background: Large language model (LLM) artificial intelligence chatbots using generative language can offer smoking cessation information and advice. However, little is known about the reliability of the information provided to users.
Objective: This study aims to examine whether 3 ChatGPT chatbots-the World Health Organization's Sarah, BeFreeGPT, and BasicGPT-provide reliable information on how to quit smoking.
State-Level Influenza Hospitalization Burden in the United States, 2022-2023.
Am J Public Health
January 2025
Alexia Couture, A. Danielle Iuliano, Ryan Threlkel, Matthew Gilmer, Alissa O'Halloran, Dawud Ujamaa, Matthew Biggerstaff, and Carrie Reed are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA. Howard H. Chang is with the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
To develop a method leveraging hospital-based surveillance to estimate influenza-related hospitalizations by state, age, and month as a means of enhancing current US influenza burden estimation efforts. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we extrapolated monthly FluSurv-NET hospitalization rates after adjusting for testing practices and diagnostic test sensitivities to non-FluSurv-NET states. We used a Poisson zero-inflated model with an overdispersion parameter within the Bayesian hierarchical framework and accounted for uncertainty and variability between states and across time.
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