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Evaluating large language models for selection of statistical test for research: A pilot study.
Perspect Clin Res
April 2024
Department of Business Data Processing and Management, Satyawati College (Eve.), University of Delhi, New Delhi, India.
Background: In contemporary research, selecting the appropriate statistical test is a critical and often challenging step. The emergence of large language models (LLMs) has offered a promising avenue for automating this process, potentially enhancing the efficiency and accuracy of statistical test selection.
Aim: This study aimed to assess the capability of freely available LLMs - OpenAI's ChatGPT3.
Perplexing paradoxical reactions: navigating the complexity of protracted tuberculosis meningitis-a case report.
Front Immunol
November 2024
National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States.
Tuberculous meningitis (TBM) has considerable mortality and morbidity, and it often presents therapeutic challenges when complicated by paradoxical reactions (PRs). Here, the clinical course of four cases of TBM patients complicated by PRs in a longitudinal TB cohort is described while also providing insights from the larger clinical cohort. Research flow cytometry, biomarker analysis, and drug concentrations were performed on available samples.
View Article and Find Full Text PDF[Miliary or disseminated tuberculosis : epidemiology, diagnosis and management].
Rev Med Liege
November 2024
Service de Pneumologie. CHU Gabès. Université de Sfax, Tunisie.
Article Synopsis
- - Military tuberculosis (MT) is a severe and potentially life-threatening form of tuberculosis characterized by widespread infection throughout the body caused by Mycobacterium tuberculosis entering the bloodstream or lymphatics.
- - Diagnosing MT is challenging because it often presents with vague symptoms and physical examination findings that can confuse even experienced doctors, while bacteriological evidence is frequently absent.
- - The prognosis for MT depends heavily on how early the disease is diagnosed and treated, emphasizing the importance of prompt medical intervention, especially in vulnerable populations like children, young adults, and those with HIV.
A Comparative Evaluation of Large Language Model Utility in Neuroimaging Clinical Decision Support.
J Imaging Inform Med
November 2024
Department of Radiology, Baltimore VA Medical Center, Baltimore, MD, USA.
Article Synopsis
- Imaging utilization is rising, but many studies may not be suitable for specific clinical situations, prompting a need for better guidance.
- This research examines the effectiveness of eight popular large language models (LLMs) in providing imaging recommendations for 24 common neuroradiology scenarios, as graded by expert neuroradiologists.
- GPT-4 and ChatGPT performed the best, with GPT-4 achieving optimal recommendations in 23 out of 24 cases, while models like Llama 2 lagged significantly behind in accuracy and usefulness.
Exploring the Spectrum of Electrolyte Imbalances in Preeclampsia: Mechanisms, Implications, and Clinical Insights.
Cureus
August 2024
Department of Obstetrics and Gynaecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND.
Preeclampsia, a complex and perplexing disorder unique to pregnancy, is widely recognized as primarily originating from placental dysfunction and can only be resolved by the delivery of the fetus in severe cases. Preeclampsia is a prevalent medical issue during pregnancy and is associated with elevated rates of maternal and infant mortality and morbidity. The exact cause of preeclampsia remains uncertain, although multiple factors have been implicated in its development based on current knowledge.
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