Findings from several studies suggest that oncology patients undergoing active treatment experience multiple symptoms and that these symptoms can have a negative effect on patient outcomes. However, no systematic review has summarized the findings from studies that assessed multiple symptoms in these patients. Therefore, the purposes of this review were to: 1) compare and contrast the characteristics of the three most commonly used instruments to measure multiple symptoms; 2) summarize the prevalence rates for multiple symptoms in studies of oncology patients receiving active treatment; 3) describe the relationships among selected demographic, disease, and treatment characteristics and multiple symptoms; and 4) describe the relationships between the occurrence of multiple symptoms and patient outcomes (i.e., functional status, quality of life). Only 18 studies were found that met the inclusion criteria for this review. The majority of the studies were cross-sectional with sample sizes that ranged from 26 to 527. Approximately 40% of patients experienced more than one symptom. However, little is known about the relationships between demographic and clinical characteristics and the occurrence of multiple symptoms. Findings from this review suggest that the occurrence of multiple symptoms is associated with decreased functional status and quality of life. However, given the large number of oncology patients who undergo active treatment each year, additional research is warranted on the prevalence and impact of multiple symptoms. Only when this descriptive research is completed with homogenous samples of patients in terms of cancer diagnoses and treatments can intervention studies for multiple symptoms be developed and tested.

This study presents the dependency of the simulation results from a global atmospheric numerical model on machines with different hardware and software systems. The global model program (GMP) of the Global/Regional Integrated Model system (GRIMs) is tested on 10 different computer systems having different central processing unit (CPU) architectures or compilers. There exist differences in the results for different compilers, parallel libraries, and optimization levels, primarily a result of the treatment of rounding errors by the different software systems. The system dependency, which is the standard deviation of the 500-hPa geopotential height averaged over the globe, increases with time. However, its fractional tendency, which is the change of the standard deviation relative to the value itself, remains nearly zero with time. In a seasonal prediction framework, the ensemble spread due to the differences in software system is comparable to the ensemble spread due to the differences in initial conditions that is used for the traditional ensemble forecasting.