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Safety of topical estrogen therapy during adjuvant endocrine treatment among patients with breast cancer: A meta-analysis based expert panel discussion.
Cancer Treat Rev
January 2025
Department of Oncology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden. Electronic address:
Importance: Endocrine treatments, such as Tamoxifen (TAM) and/or Aromatase inhibitors (AI), are the adjuvant therapy of choice for hormone-receptor positive breast cancer. These agents are associated with menopausal symptoms, adversely affecting drug compliance. Topical estrogen (TE) has been proposed for symptom management, given its' local application and presumed reduced bioavailability, however its oncological safety remains uncertain.
View Article and Find Full Text PDFDevelopment of a unified and comprehensive definition of successful spinal fusion: a systematic review.
J Neurosurg Spine
January 2025
2Cleveland Clinic Center for Spine Health, Cleveland Clinic, Cleveland; and.
Objective: Spinal fusion is a commonly performed surgical procedure used to relieve pain, deformity, and instability of various spinal pathologies. Although there have been attempts to standardize spinal fusion assessment radiologically, there is currently no unified definition that also considers clinical symptomology. This review attempts to create a more holistic and standardized definition of spinal fusion.
View Article and Find Full Text PDFThe Use of AI in Mental Health Services to Support Decision-Making: Scoping Review.
J Med Internet Res
January 2025
School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.
View Article and Find Full Text PDFAI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFMachine Learning to Predict Mortality in Older Patients With Cancer: Development and External Validation of the Geriatric Cancer Scoring System Using Two Large French Cohorts.
J Clin Oncol
January 2025
INSERM, IMRBU955, Univ Paris Est Créteil, Créteil, France.
Purpose: Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geriatric predictors. We aim to develop and externally validate a new predictive score (the Geriatric Cancer Scoring System [GCSS]) to refine individualized prognosis for older patients with cancer during the first year after a geriatric assessment (GA).
Materials And Methods: Data were collected from two French prospective multicenter cohorts of patients with cancer 70 years and older, referred for GA: ELCAPA (training set January 2007-March 2016) and ONCODAGE (validation set August 2008-March 2010).
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