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Dominik J. Ose, Richard Viskochil, Andreana N. Holowatyj, Mikaela Larson, Dalton Wilson, William A. Dunson Jr, Vikrant G. Deshmukh, J. Ryan Butcher, Belinda R. Taylor, Kim Svoboda, Jennifer Leiser, Benjamin Tingey, Benjamin Haaland, David W. Wetter, Simon J. Fisher, Mia Hashibe, and Cornelia M. Ulrich

levels) 24 , 26 or clinical studies with a small number of patients and a focus on one specific cancer. For both types of studies, the lack of cancer specifics (eg, first primary cancer), timing of DM onset, and/or treatment characteristics limit the

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Tara M. Mackay, Lennart B. van Rijssen, Jurr O. Andriessen, Mustafa Suker, Geert-Jan Creemers, Ferry A. Eskens, Ignace H. de Hingh, Lonneke V. van de Poll-Franse, Mirjam A.G. Sprangers, Olivier R. Busch, Johanna W. Wilmink, Casper H. van Eijck, Marc G. Besselink, Hanneke W. van Laarhoven, and on behalf of the Dutch Pancreatic Cancer Group

patients with pancreatic and periampullary cancer. 16 QoL may be influenced by characteristics of the healthcare organization, the patient, and the disease itself. 17 , 18 The exact relationship between patient satisfaction and QoL is unclear. Therefore

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P. Connor Johnson, Caron Jacobson, Alisha Yi, Anna Saucier, Tejaswini M. Dhawale, Ashley Nelson, Mitchell W. Lavoie, Mathew J. Reynolds, Carlisle E.W. Topping, Matthew J. Frigault, and Areej El-Jawahri

-cell therapy. We also aimed to examine associations among patient and clinical characteristics and important EoL outcomes. Data depicting patients’ healthcare utilization and EoL care could allow clinicians to communicate important information about the

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Jessica D. McDermott, Megan Eguchi, Rustain Morgan, Arya Amini, Julie A. Goddard, Evelinn A. Borrayo, and Sana D. Karam

modern era. We used updated SEER-Medicare population data to derive information on patient and tumor characteristics and treatment, payment, and healthcare use data to control for bias of different healthcare/insurance coverage. We focused our comparisons

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Joseph A. Greer, Beverly Moy, Areej El-Jawahri, Vicki A. Jackson, Mihir Kamdar, Juliet Jacobsen, Charlotta Lindvall, Jennifer A. Shin, Simone Rinaldi, Heather A. Carlson, Angela Sousa, Emily R. Gallagher, Zhigang Li, Samantha Moran, Magaret Ruddy, Maya V. Anand, Julia E. Carp, and Jennifer S. Temel

characteristics, we first calculated descriptive statistics. Following the intent-to-treat principle, we used the Fisher exact test and logistic and linear regression modeling to examine group differences in rates of documentation of EoL care discussions and time

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William Alegria, Bernard L. Marini, Kevin Sellery Gregg, Dale Lee Bixby, Anthony Perissinotti, and Jerod Nagel

characteristics. Dichotomous variables were compared using a Pearson chi-square test or Fisher exact test when appropriate. Continuous variables with nonparametric distributions were expressed as medians with corresponding interquartile ranges, and categorical

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characteristics and overall treatment patterns of patients diagnosed with mNETs from a retrospective database analysis of community oncology practices. This report provides additional information on the treatment outcomes for this population. Methods: Clinical

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Katy E. Balazy, Cecil M. Benitez, Paulina M. Gutkin, Clare E. Jacobson, Rie von Eyben, and Kathleen C. Horst

treatments, and there was no difference between NES and ES patients ( P =.794). Table 1. Baseline Patient Characteristics Of the 5 time intervals analyzed, the longest interval was from resection to start of RT (mean [SD], 2.8 [1.8] months), and

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Mei-Chin Hsieh, Lu Zhang, Xiao-Cheng Wu, Mary B. Davidson, Michelle Loch, and Vivien W. Chen

’ sociodemographic and tumor characteristics, and to examine the impact of guideline-concordant treatment status and cancer subtype on survival outcome. Methods Data Source and Study Cohort Data on female breast cancer were obtained from the Louisiana Tumor Registry

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Eric D. Miller, Ansel P. Nalin, Dayssy A. Diaz Pardo, Andrea L. Arnett, Emily Huang, Alessandra C. Gasior, Pannaga Malalur, Hui-Zi Chen, Terence M. Williams, and Jose G. Bazan

account for clustering in matched pairs. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc). Results Baseline Characteristics We identified 9,156 elderly patients and 17,640 nonelderly patients who met the study criteria