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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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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

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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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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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Eva Battaglini, David Goldstein, Peter Grimison, Susan McCullough, Phil Mendoza-Jones, and Susanna B. Park

were calculated for demographic and clinical characteristics. For the comparison of respondents reporting current CIPN symptoms to those without symptoms, t tests were used for continuous variables and chi-square tests were used for categorical

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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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James Sun, Brittany J. Mathias, Christine Laronga, Weihong Sun, Jun-Min Zhou, William J. Fulp, John V. Kiluk, and M. Catherine Lee

%) had additional nodal disease. Table 1. Demographic and Clinicopathologic Characteristics In terms of adjuvant therapy, 176 (53%) of 329 patients received RT, 245 (75%) received chemotherapy, and 272 (83%) received endocrine therapy ( Table 1 ). Records

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Daniëlle D. Huijts, Onno R. Guicherit, Jan Willem T. Dekker, Julia T. van Groningen, Leti van Bodegom-Vos, Esther Bastiaannet, Johannes A. Govaert, Michel W. Wouters, and Perla J. Marang-van de Mheen

considered colon and rectal cancer together, whereas patient characteristics, complication risks, and clinical outcomes are different in colon versus rectal cancer. 10 Furthermore, to provide clues for possible quality improvement, other outcomes require

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Barbara Dull, Andrew Linkugel, Julie A. Margenthaler, and Amy E. Cyr

recorded, as were demographic data and tumor characteristics, such as pathology size and tumor marker profile. Statistical analysis was performed using Fisher exact test and Student unpaired t -test, with a P value <.05 being considered statistically

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Owen Tan, Deborah J. Schofield, and Rupendra Shrestha

Patient Characteristics Hospital and ED Costs Hospital treatment costs were estimated using Australian Refined Diagnosis Related Groups (AR-DRGs) and episode of care LoS. Cost estimates were obtained from the National Hospital Cost Data