Objective: To determine whether there is a survival difference for African-American men (AAM) versus Caucasian American men (CM) with penile squamous cell carcinoma (pSCC), particularly in locally advanced and metastatic cases where disease mortality is highest.
Patients And Methods: Using the Florida Cancer Data System, we identified men with pSCC from 2005 to 2013. We compared age, follow-up, stage, race, and treatment type between AAM and CM. We performed Kaplan-Meier analysis for overall survival (OS) between AAM and CM for all stages, and for those with locally advanced and metastatic disease. A multivariable model was developed to determine significant predictors of OS.
Results: In all, 653 men (94 AAM and 559 CM) had pSCC and 198 (30%) had locally advanced and/or metastatic disease. A higher proportion of AAM had locally advanced and/or metastatic disease compared to CM (38 [40%] vs 160 [29%], P = 0.03). The median (interquartile range) follow-up for the entire cohort was 12.6 (5.4-32.0) months. For all stages, AAM had a significantly lower median OS compared to CM (26 vs 36 months, P = 0.03). For locally advanced and metastatic disease, there was a consistent trend toward disparity in median OS between AAM and CM (17 vs 22 months, P = 0.06). After adjusting for age, stage, grade, and treatment type, AAM with pSCC had a greater likelihood of death compared to CM (hazard ratio 1.64, P = 0.014).
Conclusions: AAM have worse OS compared to CM with pSCC and this may partly be due to advanced stage at presentation. Treatment disparity may also contribute to lessened survival in AAM, but we were unable to demonstrate a significant difference in treatment utilisation between the groups.
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http://dx.doi.org/10.1111/bju.14110 | DOI Listing |
J Orthop Surg Res
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The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, No.41 Linyin Road, Baotou, Inner Mongolia, 014010, China.
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In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP.
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