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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.

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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.

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Objectives: To determine the top 100 cited authors and the top 20 articles in the Journal of Orthopaedic Trauma (JOT) and compare its impact factor to orthopaedic and non-orthopaedic surgery literature.

Design: Review.

Methods: The Web of Science database was used to determine the top 100 cited authors and top 20 cited articles that originated in JOT from 1995 to the present.

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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.

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Background: The neonatal mortality rate in Pakistan is the third highest in Asia, with 8.6 million preterm babies. These newborns require warmth, nutrition, and infection protection, typically provided by incubators.

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