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Objective: To evaluate the association between self-perceived use of shared decision-making among urologists with use of validated prediction tools and self-described surgical decision-making.
Methods: This is a convergent mixed methods study of these parallel data from two modules (Shared Decision Making and Validated Prediction tools) within the 2019 American Urological Association (AUA) Annual Census. The shared decision-making (SDM) module queried aspects of SDM that urologists regularly used. The validated prediction tools module queried whether urologists regularly used, trusted, and found prediction tools helpful. Selected respondents to the 2019 AUA Annual Census underwent qualitative interviews on their surgical decision-making.
Results: In the weight sampled of 12,312 practicing urologists, most (77%) reported routine use of SDM, whereas only 30% noted regular use of validated prediction tools. On multivariable analysis, users of prediction tools were not associated with regular SDM use (31% vs 28%, P = .006) though was associated with use of decision aids f (32% vs 26%, P < .001). Shared decision-making emerged thematically with respect to matching treatment options, prioritizing goals, and navigating challenging decisions. However, the six specific components of shared decision-making ranged in their mentions within qualitative interviews.
Conclusion: Most urologists report performing SDM as supported by its thematic presence in surgical decision-making. However, only a minority use validated prediction tools and urologists infrequently mention specific SDM components. This discrepancy provides an opportunity to explore how urologists perform SDM and can be used to support integrated strategies to implement SDM more effectively in clinical practice.
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http://dx.doi.org/10.1016/j.urology.2023.10.026 | DOI Listing |
JMIR Med Inform
September 2025
College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.
Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.
View Article and Find Full Text PDFAm J Physiol Regul Integr Comp Physiol
September 2025
Department of Pediatrics, Ribeirao Preto Medical School - University of Sao Paulo, Brazil.
The differential diagnosis within polyuria-polydipsia syndrome, especially in the pediatric population, remains challenging. Despite its limited accuracy, the water deprivation test (WDT) is the reference test in pediatrics. We retrospectively analyzed performed in 65 pediatric patients (mean age 8.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Medicine, The Red Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Background: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.
Methods: This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD.
J Alzheimers Dis
September 2025
Department of Medicine and Surgery, Unit of Neurology, Neurophysiology, Neurobiology and Psychiatry, Università Campus Bio-Medico di Roma, Roma, Italy.
BackgroundAlzheimer's disease (AD) is the most common neurodegenerative disorder. While AD diagnosis traditionally relies on clinical criteria, recent trends favor a precise biological definition. Existing biomarkers efficiently detect AD pathology but inadequately reflect the extent of cognitive impairment or disease heterogeneity.
View Article and Find Full Text PDFJAMA Dermatol
September 2025
Department of Population Health, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia.
Importance: Increasingly, strategies to systematically detect melanomas invoke targeted approaches, whereby those at highest risk are prioritized for skin screening. Many tools exist to predict future melanoma risk, but most have limited accuracy and are potentially biased.
Objectives: To develop an improved melanoma risk prediction tool for invasive melanoma.