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Advantages and Inconveniences of a Multi-Agent Large Language Model System to Mitigate Cognitive Biases in Diagnostic Challenges.
J Med Internet Res
January 2025
Medical Information Department, Civil Hospices of Lyon, Lyon, France.
Comparison of spectacle lenses with highly aspherical lenslets versus orthokeratology for the management of axial length elongation.
Clin Exp Optom
January 2025
Aier Eye Hospital, Jinan University, Guangzhou, China.
Clinical Relevance: When selecting an intervention for myopia management, parental inquiries centre around the comparative efficacy of orthokeratology versus myopic defocus spectacle lenses. This prompts an intriguing investigation into the nuanced differences between these two treatment methods.
Background: This study aimed to compare the efficacy of spectacle lenses with highly aspherical lenslets (HAL) versus orthokeratology (Ortho-k) in controlling axial length elongation.
The Impact of Electrophysiological Diversity on Pattern Completion in Lithium Nonresponsive Bipolar Disorder: A Computational Modeling Approach.
Brain Behav
January 2025
Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.
Introduction: Patients with bipolar disorder (BD) demonstrate episodic memory deficits, which may be hippocampal-dependent and may be attenuated in lithium responders. Induced pluripotent stem cell-derived CA3 pyramidal cell-like neurons show significant hyperexcitability in lithium-responsive BD patients, while lithium nonresponders show marked variance in hyperexcitability. We hypothesize that this variable excitability will impair episodic memory recall, as assessed by cued retrieval (pattern completion) within a computational model of the hippocampal CA3.
View Article and Find Full Text PDFDeep learning-based MVIT-MLKA model for accurate classification of pancreatic lesions: a multicenter retrospective cohort study.
Radiol Med
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Background: Accurate differentiation between benign and malignant pancreatic lesions is critical for effective patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography (CT) images to predict the classification of pancreatic lesions.
Methods: This retrospective study included 864 patients (422 men, 442 women) with confirmed histopathological results across three medical centers, forming a training cohort, internal testing cohort, and external validation cohort.
Validating the accuracy of mathematical model-based pharmacogenomics dose prediction with real-world data.
Eur J Clin Pharmacol
January 2025
Electrical and Computer Engineering Department, School of Engineering, Lebanese American University, P.O. Box: 36, Byblos, F-19, Lebanon.
Objective: The study aims to verify the usage of mathematical modeling in predicting patients' medication doses in association with their genotypes versus real-world data.
Methods: The work relied on collecting, extracting, and using real-world data on dosing and patients' genotypes. Drug metabolizing enzymes, i.
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