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A family-based behavioral group obesity randomized control feasibility trial across a clinical trials network: a focus on contact hours.
J Pediatr Psychol
January 2025
Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, United States.
Objective: This ancillary study's purpose is to describe the relationship between dose of treatment and body mass index (BMI) outcomes in a tele-behavioral health program delivered in the IDeA States Pediatric Clinical Trials Network to children and their families living in rural communities.
Methods: Participants randomized to the intervention were able to receive 26 contact hours (15 hr of group sessions and 11 hr of individual sessions) of material focused on nutrition, physical activity, and behavioral caregiver training delivered via interactive televideo. Dose of the intervention received by child/caregiver dyads (n = 52) from rural areas was measured as contact hours.
Background: The purpose of this study was to evaluate the performance and evolution of Chat Generative Pre-Trained Transformer (ChatGPT; OpenAI) as a resource for shoulder and elbow surgery information by assessing its accuracy on the American Academy of Orthopaedic Surgeons shoulder-elbow self-assessment questions. We hypothesized that both ChatGPT models would demonstrate proficiency and that there would be significant improvement with progressive iterations.
Materials And Methods: A total of 200 questions were selected from the 2019 and 2021 American Academy of Orthopaedic Surgeons shoulder-elbow self-assessment questions.
Transformers for Neuroimage Segmentation: Scoping Review.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFAn alternative approach to biokinetic modelling for phenol pollutant degradation and microbial growth using Genetic Programming.
Environ Technol
January 2025
Centre for Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, India.
Biokinetic models can optimise pollutant degradation and enhance microbial growth processes, aiding to protect ecosystem protection. Traditional biokinetic approaches (such as Monod, Haldane, etc.) can be challenging, as they require detailed knowledge of the organism's metabolism and the ability to solve numerous kinetic differential equations based on the principles of micro, molecular biology and biochemistry (first engineering principles) which can lead to discrepancies between predicted and actual degradation rates.
View Article and Find Full Text PDFExploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
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