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Seanthel Delos Santos, Noah Witzke, Bishal Gyawali, Vanessa Sarah Arciero, Amanda Putri Rahmadian, Louis Everest, Matthew C. Cheung, and Kelvin K. Chan

assessed at the time of primary publication and at 3 years post-FDA approval using all publicly available data in eligible subsequent publications. Results Characteristics of Included Studies Of 113 indications that met the eligibility criteria, there were

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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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Carrie Zornosa, Jonathan L. Vandergrift, Gregory P. Kalemkerian, David S. Ettinger, Michael S. Rabin, Mary Reid, Gregory A. Otterson, Marianna Koczywas, Thomas A. D'Amico, Joyce C. Niland, Rizvan Mamet, and Katherine M. Pisters

Member Institution. Data collection includes patient, disease, and treatment characteristics that are manually abstracted from medical records by trained data managers at each participating site. Data are abstracted for each patient in 6-month intervals

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Ishveen Chopra, Malcolm D. Mattes, Patricia Findley, Xi Tan, Nilanjana Dwibedi, and Usha Sambamoorthi

service. Each individual was observed for 48 months ( Figure 1 ). Figure 1. Schematic of the study design. Each individual was observed for 48 months with a 24-month baseline (for identification of CAD and baseline characteristics), 12-month preindex, and

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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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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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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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Lauren E. Kernochan and Rochelle L. Garcia

Edited by Kerrin G. Robinson

Carcinosarcoma of the uterus (malignant mixed Müllerian tumor [MMMT]) is an uncommon, typically extremely aggressive neoplasm histologically composed of malignant epithelial and mesenchymal (stromal) elements. Although the literature contains some debate, most authors now agree that most MMMTs derive from sarcomatous differentiation in a high-grade carcinoma. This article reviews the clinical and histopathologic features of this interesting neoplasm, with particular emphasis on recent data supporting MMMTs as primarily epithelial malignant neoplasms with areas of mesenchymal/spindle cell differentiation.