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Pamala A. Pawloski, Gabriel A. Brooks, Matthew E. Nielsen, and Barbara A. Olson-Bullis

Background Clinical decision support (CDS) systems include any electronic system designed to directly aid clinical decision-making by using individual patient characteristics to generate patient-specific assessments or recommendations. 1 , 2 These

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Ashish Rai, Xuesong Han, Zhiyuan Zheng, K. Robin Yabroff, and Ahmedin Jemal

characteristics (year 1 sociodemographic features, insurance status, usual source of care, comorbidity count, smoking status, active treatment status, tertile of office visits, any ED visits, and any inpatient care) and membership of the highest satisfaction

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Zhong Ye, Chun Wang, Limin Guo, Juan P. Palazzo, Zhixing Han, Yinzhi Lai, Jing Jiang, James A. Posey, Atrayee Basu Mallick, Bingshan Li, Li Jiang, and Hushan Yang

characteristic (ROC) curves and calculating the area under the curve (AUC). The prediction power was also estimated using Concordance-index (C-index), which works as an extension of AUC to the case of censored survival data. Patients with CRC were divided into

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Erwei Zeng, Wei He, Karin E. Smedby, and Kamila Czene

Sweden, collecting detailed information on tumor characteristics and treatments. 15 , 16 The Swedish Prescribed Drug Register (SPDR) is a nationwide database that records all dispensed prescription drugs in pharmacies since July 2005, with <0.3% of data

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U-Syn Ha, Jin Bong Choi, Jung Im Shim, Minjoo Kang, Eunjung Park, Shinhee Kang, Jooyeon Park, Jangmi Yang, Insun Choi, Jeonghoon Ahn, Cheol Kwak, Chang Wook Jeong, Choung Soo Kim, Seok-Soo Byun, Seong Il Seo, Hyun Moo Lee, Seung-Ju Lee, Seung Hwan Lee, Byung Ha Chung, and Ji Youl Lee

physical activity. Because placement in the PADT group was strongly associated with patient characteristics, we used PSM analysis to balance covariates between the groups. We stratified by age and summary cancer stages to account for the clinical

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Jennifer W. Mack, Erin R. Currie, Vincent Martello, Jordan Gittzus, Asisa Isack, Lauren Fisher, Lisa C. Lindley, Stephanie Gilbertson-White, Eric Roeland, and Marie Bakitas

( Table 3 ). Nearly half (46%) had Medicaid or other public insurance, and 54% lived in a low-income zip code (median income ≤200% FPL). Nearly half of caregivers were college graduates (46%). Table 2. AYA Caregiver Characteristics Table

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Tara M. Mackay, Anouk E.J. Latenstein, Mirjam A.G. Sprangers, Lydia G. van der Geest, Geert-Jan Creemers, Susan van Dieren, Jan-Willem B. de Groot, Bas Groot Koerkamp, Ignace H. de Hingh, Marjolein Y.V. Homs, Evelien J.M. de Jong, I. Quintus Molenaar, Gijs A. Patijn, Lonneke V. van de Poll-Franse, Hjalmar C. van Santvoort, Judith de Vos-Geelen, Johanna W. Wilmink, Casper H. van Eijck, Marc G. Besselink, Hanneke W.M. van Laarhoven, and for the Dutch Pancreatic Cancer Group

treatment characteristics, such as date of diagnosis, age at diagnosis, sex, body mass index, comorbidities, ECOG performance status, pathologic diagnosis, tumor location, tumor stage (according to AJCC, 7th edition), tumor size, tumor differentiation grade

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Marsha Reyngold, Joyce Niland, Anna ter Veer, Dana Milne, Tanios Bekaii-Saab, Steven J. Cohen, Lily Lai, Deborah Schrag, John M. Skibber, William Small Jr, Martin Weiser, Neal Wilkinson, and Karyn A. Goodman

Demographic characteristics potentially associated with receipt of RT chosen for analysis included age at diagnosis, gender, racial/ethnic background, type of insurance (private, Medicare, Medicaid), household income, NCCN Member Institution, distance to the

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Lindsay J. Collin, Ming Yan, Renjian Jiang, Keerthi Gogineni, Preeti Subhedar, Kevin C. Ward, Jeffrey M. Switchenko, Joseph Lipscomb, Jasmine Miller-Kleinhenz, Mylin A. Torres, Jolinta Lin, and Lauren E. McCullough

cancer outcomes. 10 – 13 Nonadherence to guidelines could arise from multiple factors, including structural racism, barriers to access, tumor and patient characteristics, or clinician and patient preferences. 11 Therefore, nonadherence to clinical

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Davinia S.E. Seah, Ines Vaz Luis, Erin Macrae, Jessica Sohl, Georgia Litsas, Eric P. Winer, Nancy U. Lin, and Harold J. Burstein

subtypes. 14 Decisions about treatment are increasingly being tailored to individual patient characteristics, such as tumor subtype. This clinical classification identifies targets with established data on treatment efficacy; hormonal therapies for HR