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Trends, characteristics, and outcomes of pregnancy in women with attention-deficit hyperactivity disorder: A nationwide analysis.

Eur J Obstet Gynecol Reprod Biol

January 2025

Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles, CA, USA; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Los Angeles General Medical Center, Los Angeles, CA, USA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA. Electronic address:

Objective: To assess clinical and obstetric characteristics associated with pregnant patients with a diagnosis of attention-deficit hyperactivity disorder (ADHD).

Methods: This serial cross-sectional study queried the Agency of Healthcare Research and Quality's Healthcare Cost and Utilization Project National Inpatient Sample. The study population was 16,759,786 hospital deliveries from 2016 to 2020.

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Background: Long-leg alignment and joint line obliquity have traditionally been assessed using two-dimensional (2D) radiography, but the accuracy of this measurement has remained unclear. This study aimed to evaluate the accuracy of 2D measurements of lateral distal femoral angle (LDFA) and medial proximal tibial angle (MPTA) using upright three-dimensional (3D) computed tomography (CT).

Methods: This study involved 66 knees from 38 patients (34 women, four men) with knee osteoarthritis (OA), categorized by Kellgren-Lawrence (KL) grade.

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Background: Sarcoidosis, a granulomatous inflammatory disease, exhibits diverse clinical manifestations, often affecting multiple organs. Diagnostic challenges arise due to its similarities with tuberculosis, particularly in high-burden areas. Differentiating between the two relies on clinical judgment, laboratory tests, imaging, and invasive procedures.

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Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.

Int J Med Inform

January 2025

Rheumatology and Allergy Clinical Epidemiology Research Center and Division of Rheumatology, Allergy, and Immunology, and Mongan Institute, Department of Medicine, Massachusetts General Hospital Boston MA USA. Electronic address:

Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.

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