Does quality of life among breast cancer survivors one year after diagnosis differ depending on urban and non-urban residence? A comparative study

Background: This study examined the quality of life (QOL), measured by the Functional Assessment of Cancer Therapy (FACT) questionnaire, among urban (n=277) and non-urban (n=323) breast cancer survivors and women from the general population (n=1140) in Queensland, Australia. ---------- Methods: Population-based samples of breast cancer survivors aged <75 years who were 12 months post-diagnosis and similarly-aged women from the general population were recruited between 2002 and 2007. ---------- Results: Age-adjusted QOL among urban and non-urban breast cancer survivors was similar, although QOL related to breast cancer concerns was the weakest domain and was lower among non-urban survivors than their urban counterparts (36.8 versus 40.4, P<0.01). Irrespective of residence, breast cancer survivors, on average, reported comparable scores on most QOL scales as their general population peers, although physical well-being was significantly lower among non-urban survivors (versus the general population, P<0.01). Overall, around 20%-33% of survivors experienced lower QOL than peers without the disease. The odds of reporting QOL below normative levels were increased more than two-fold for those who experienced complications following surgery, reported upper-body problems, had higher perceived stress levels and/or a poor perception of handling stress (P<0.01 for all). ---------- Conclusions: Results can be used to identify subgroups of women at risk of low QOL and to inform components of tailored recovery interventions to optimize QOL for these women following cancer treatment.

An advanced implicit meshless approach for the non-linear anomalous subdiffusion equation

Recently, the numerical modelling and simulation for anomalous subdiffusion equation (ASDE), which is a type of fractional partial differential equation( FPDE) and has been found with widely applications in modern engineering and sciences, are attracting more and more attentions. The current dominant numerical method for modelling ASDE is Finite Difference Method (FDM), which is based on a pre-defined grid leading to inherited issues or shortcomings. This paper aims to develop an implicit meshless approach based on the radial basis functions (RBF) for numerical simulation of the non-linear ASDE. The discrete system of equations is obtained by using the meshless shape functions and the strong-forms. The stability and convergence of this meshless approach are then discussed and theoretically proven. Several numerical examples with different problem domains are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. The results obtained by the meshless formulations are also compared with those obtained by FDM in terms of their accuracy and efficiency. It is concluded that the present meshless formulation is very effective for the modeling and simulation of the ASDE. Therefore, the meshless technique should have good potential in development of a robust simulation tool for problems in engineering and science which are governed by the various types of fractional differential equations.

Use of face masks by non-scrubbed operating room staff : a randomized controlled trial.

Background: Ambiguity remains about the effectiveness of wearing surgical face masks. The purpose of this study was to assess the impact on surgical site infections when non-scrubbed operating room staff did not wear surgical face masks. Design: Randomised controlled trial. Participants: Patients undergoing elective or emergency obstetric, gynecological, general, orthopaedic, breast or urological surgery in an Australian tertiary hospital. Intervention: 827 participants were enrolled and complete follow-up data was available for 811 (98.1%) patients. Operating room lists were randomly allocated to a ‘Mask roup’ (all non-scrubbed staff wore a mask) or ‘No Mask group’ (none of the non-scrubbed staff wore masks). Primary end point: Surgical site infection (identified using in-patient surveillance; post discharge follow-up and chart reviews). The patient was followed for up to six weeks. Results: Overall, 83 (10.2%) surgical site infections were recorded; 46/401 (11.5%) in the Masked group and 37/410 (9.0%) in the No Mask group; odds ratio (OR) 0.77 (95% confidence interval (CI) 0.49 to 1.21), p = 0.151. Independent risk factors for surgical site infection included: any pre-operative stay (adjusted odds ratio [aOR], 0.43 (95% CI, 0.20; 0.95), high BMI aOR, 0.38 (95% CI, 0.17; 0.87), and any previous surgical site infection aOR, 0.40 (95% CI, 0.17; 0.89). Conclusion: Surgical site infection rates did not increase when non-scrubbed operating room personnel did not wear a face mask.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

The patentability of non-physical inventions : lessons from the United States

Patent systems around the world are being pressed to recognise and protect challengingly new and exciting subject matter in order to keep pace with the rapid technological advancement of our age and the fact we are moving into the era of the ‘knowledge economy’. This rapid development and pressure to expand the bounds of what has traditionally been recognised as patentable subject matter has created uncertainty regarding what it is that the patent system is actually supposed to protect. Among other things, the patent system has had to contend with uncertainty surrounding claims to horticultural and agricultural methods, artificial living micro-organisms, methods of treating the human body, computer software and business methods. The contentious issue of the moment is one at whose heart lies the important distinction between what is a mere abstract idea and what is properly an invention deserving of the monopoly protection afforded by a patent. That question is whether purely intangible inventions, being methods that do not involve a physical aspect or effect or cause a physical transformation of matter, constitute patentable subject matter. This paper goes some way to addressing these uncertainties by considering how the Australian approach to the question can be informed by developments arising in the United States of America, and canvassing some of the possible lessons we in Australia might learn from the approaches taken thus far in the United States.

Advanced analysis of steel frame structures comprising non-compact sections

During the past decade, a significant amount of research has been conducted internationally with the aim of developing, implementing, and verifying "advanced analysis" methods suitable for non-linear analysis and design of steel frame structures. Application of these methods permits comprehensive assessment of the actual failure modes and ultimate strengths of structural systems in practical design situations, without resort to simplified elastic methods of analysis and semi-empirical specification equations. Advanced analysis has the potential to extend the creativity of structural engineers and simplify the design process, while ensuring greater economy and more uniform safety with respect to the ultimate limit state. The application of advanced analysis methods has previously been restricted to steel frames comprising only members with compact cross-sections that are not subject to the effects of local buckling. This precluded the use of advanced analysis from the design of steel frames comprising a significant proportion of the most commonly used Australian sections, which are non-compact and subject to the effects of local buckling. This thesis contains a detailed description of research conducted over the past three years in an attempt to extend the scope of advanced analysis by developing methods that include the effects of local buckling in a non-linear analysis formulation, suitable for practical design of steel frames comprising non-compact sections. Two alternative concentrated plasticity formulations are presented in this thesis: the refined plastic hinge method and the pseudo plastic zone method. Both methods implicitly account for the effects of gradual cross-sectional yielding, longitudinal spread of plasticity, initial geometric imperfections, residual stresses, and local buckling. The accuracy and precision of the methods for the analysis of steel frames comprising non-compact sections has been established by comparison with a comprehensive range of analytical benchmark frame solutions. Both the refined plastic hinge and pseudo plastic zone methods are more accurate and precise than the conventional individual member design methods based on elastic analysis and specification equations. For example, the pseudo plastic zone method predicts the ultimate strength of the analytical benchmark frames with an average conservative error of less than one percent, and has an acceptable maximum unconservati_ve error of less than five percent. The pseudo plastic zone model can allow the design capacity to be increased by up to 30 percent for simple frames, mainly due to the consideration of inelastic redistribution. The benefits may be even more significant for complex frames with significant redundancy, which provides greater scope for inelastic redistribution. The analytical benchmark frame solutions were obtained using a distributed plasticity shell finite element model. A detailed description of this model and the results of all the 120 benchmark analyses are provided. The model explicitly accounts for the effects of gradual cross-sectional yielding, longitudinal spread of plasticity, initial geometric imperfections, residual stresses, and local buckling. Its accuracy was verified by comparison with a variety of analytical solutions and the results of three large-scale experimental tests of steel frames comprising non-compact sections. A description of the experimental method and test results is also provided.