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| http://dx.doi.org/10.3389/fnins.2024.1524097 | DOI Listing |
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Lymph node assessment at the time of robotic hysterectomy for endometrial intraepithelial neoplasia: A cost-effectiveness analysis.
Gynecol Oncol
January 2025
Division of Gynecologic Oncology, St. Luke's University Health Network, Bethlehem, PA, United States of America.
Objective: We sought to determine the cost-effectiveness (CE) of lymph node dissection (LND) at the time of hysterectomy for endometrial intraepithelial neoplasia (EIN).
Methods: A decision analytic model was created to evaluate the strategies of routine full LND, sentinel lymph node dissection (SNLD), SNLD without advancing to full LND in the event of non-mapping, and full LND based on Mayo Criteria, versus no LND. Patients in the no LND group and those in the SLND group without advancement to full LND in the event of non-mapping who were found to have EC on final pathology and suspicious post-operative imaging underwent full LND.
Added prognostic value of sentinel lymph node mapping in endometrial cancer to molecular subgroups.
Gynecol Oncol
January 2025
Department of Obstetrics and Gynecology, Bern University Hospital and University of Bern, Bern, Switzerland.
Objective: Treatment approaches for endometrial cancer became more personalized in the last decade, mainly due to two key advancements - sentinel lymph node (SLN) mapping and molecular classification. However, their prognostic interaction remains relatively unexplored.
Methods: This retrospective cohort study included patients with endometrial cancer, who underwent surgical treatment including SLN mapping at the Bern University Hospital, Switzerland.
Thoracic anesthesia, ventilation, and oxygen delivery: 'A review of the recent advancements'.
Best Pract Res Clin Anaesthesiol
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Yale School of Medicine, USA.
Machine learning applications in healthcare clinical practice and research.
World J Clin Cases
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Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
View Article and Find Full Text PDFAdvancement in utilization of magnetic resonance imaging and biomarkers in the understanding of schizophrenia.
World J Clin Cases
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Department of Psychiatry and Behavioral Health, Mercyhealth Hospital and Trauma Center, Janesville, WI 53548, United States.
Historically, psychiatric diagnoses have been made based on patient's reported symptoms applying the criteria from diagnostic and statistical manual of mental disorders. The utilization of neuroimaging or biomarkers to make the diagnosis and manage psychiatric disorders remains a distant goal. There have been several studies that examine brain imaging in psychiatric disorders, but more work is needed to elucidate the complexities of the human brain.
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