Narratives of mitigation : sentencing Indigenous criminal defendants in South Australia’s Higher Courts

In their statistical analyses of higher court sentencing in South Australia, Jeffries and Bond (2009) found evidence that Indigenous offenders were treated more leniently than non-Indigenous offenders, when they appeared before the court under similar numerical circumstances. Using a sample of narratives for criminal defendants convicted in South Australia’s higher courts, the current article extends Jeffries and Bond’s (2009) prior statistical work by drawing on the ‘focal concerns’ approach to establish whether, and in what ways, Indigeneity comes to exert a mitigating influence over sentencing. Results show that the sentencing stories of Indigenous and non-Indigenous offenders differed in ways that may have reduced assessments of blameworthiness and risk for Indigenous defendants. In addition, judges highlighted a number of Indigenous-specific constraints that potentially could result in imprisonment being construed as an overly harsh and costly sentence for Indigenous offenders.

Implementation Guide for Surveillance of Clostridium Difficile Infection -- [Consultation Edition]

The Implementation Guide for hospital surveillance of Clostridium difficile infection (CDI) has been produced by the Healthcare Associated Infection (HAI) Technical Working Group of the Australian Commission on Safety and Quality in Health Care (ACSQHC), and endorsed by the HAI Advisory Group. State jurisdictions and the ACSQHC have representatives on the Technical Working Group, and have had input into this document. (See acknowledgements on inside front cover)...

Feasibility of using telecommunications technology in a Cardiac-Diabetes Self-Management Program

Background: Evidence demonstrates self-management programs are an effective approach to assist patients with chronic diseases such as type 2 diabetes or cardiac conditions to modify their lifestyle for better managing their conditions. Using information technology (IT) has great potential to support self-management programs and assist patients to fulfill their goals in managing their conditions more efficiently and effectively. Examples of different types of technology used in self-management programs that have limited research support include: text messages, telephone followup, web-based programs, and other internet-assisted education. But little is known about the applicability and feasiability of different forms of technology for patients with chronic diseases such as those with type 2 diabetes and critical cardiac conditions. Furthermore, although there is some evidence of the benefits of using IT in supporting self-management programs, further research on the use of IT in such programs is recommended. Objective: To develop and pilot test an integrated Cardiac- Diabetes Self-Management Program (CDSMP) incorporating telephone and text-message follow-up. Methods: A pilot study using randomised controlled trial is conducted in the coronary care unit (CCU) in a Brisbane metropolitan hospital in Australia to collect data on patients with type 2 diabetes admitted to CCU. The main outcomes included self-efficacy levels, knowledge, and quality of life. Results: Initial results reveal that patients with diabetes admitted to the CCU in the experimental group did improve their self-efficacy, and knowledge levels. Acknowledgements: This Project is funded by QUT Early Career Researcher Research Grant

Effect of CT electron density identification on Monte Carlo simulations of radiotherapeutic treatments

Introduction: The accurate identification of tissue electron densities is of great importance for Monte Carlo (MC) dose calculations. When converting patient CT data into a voxelised format suitable for MC simulations, however, it is common to simplify the assignment of electron densities so that the complex tissues existing in the human body are categorized into a few basic types. This study examines the effects that the assignment of tissue types and the calculation of densities can have on the results of MC simulations, for the particular case of a Siemen’s Sensation 4 CT scanner located in a radiotherapy centre where QA measurements are routinely made using 11 tissue types (plus air). Methods: DOSXYZnrc phantoms are generated from CT data, using the CTCREATE user code, with the relationship between Hounsfield units (HU) and density determined via linear interpolation between a series of specified points on the ‘CT-density ramp’ (see Figure 1(a)). Tissue types are assigned according to HU ranges. Each voxel in the DOSXYZnrc phantom therefore has an electron density (electrons/cm3) defined by the product of the mass density (from the HU conversion) and the intrinsic electron density (electrons /gram) (from the material assignment), in that voxel. In this study, we consider the problems of density conversion and material identification separately: the CT-density ramp is simplified by decreasing the number of points which define it from 12 down to 8, 3 and 2; and the material-type-assignment is varied by defining the materials which comprise our test phantom (a Supertech head) as two tissues and bone, two plastics and bone, water only and (as an extreme case) lead only. The effect of these parameters on radiological thickness maps derived from simulated portal images is investigated. Results & Discussion: Increasing the degree of simplification of the CT-density ramp results in an increasing effect on the resulting radiological thickness calculated for the Supertech head phantom. For instance, defining the CT-density ramp using 8 points, instead of 12, results in a maximum radiological thickness change of 0.2 cm, whereas defining the CT-density ramp using only 2 points results in a maximum radiological thickness change of 11.2 cm. Changing the definition of the materials comprising the phantom between water and plastic and tissue results in millimetre-scale changes to the resulting radiological thickness. When the entire phantom is defined as lead, this alteration changes the calculated radiological thickness by a maximum of 9.7 cm. Evidently, the simplification of the CT-density ramp has a greater effect on the resulting radiological thickness map than does the alteration of the assignment of tissue types. Conclusions: It is possible to alter the definitions of the tissue types comprising the phantom (or patient) without substantially altering the results of simulated portal images. However, these images are very sensitive to the accurate identification of the HU-density relationship. When converting data from a patient’s CT into a MC simulation phantom, therefore, all possible care should be taken to accurately reproduce the conversion between HU and mass density, for the specific CT scanner used. Acknowledgements: This work is funded by the NHMRC, through a project grant, and supported by the Queensland University of Technology (QUT) and the Royal Brisbane and Women's Hospital (RBWH), Brisbane, Australia. The authors are grateful to the staff of the RBWH, especially Darren Cassidy, for assistance in obtaining the phantom CT data used in this study. The authors also wish to thank Cathy Hargrave, of QUT, for assistance in formatting the CT data, using the Pinnacle TPS. Computational resources and services used in this work were provided by the HPC and Research Support Group, QUT, Brisbane, Australia.

Phantom Scatter and the A-SI EPID

Introduction: The use of amorphous-silicon electronic portal imaging devices (a-Si EPIDs) for dosimetry is complicated by the effects of scattered radiation. In photon radiotherapy, primary signal at the detector can be accompanied by photons scattered from linear accelerator components, detector materials, intervening air, treatment room surfaces (floor, walls, etc) and from the patient/phantom being irradiated. Consequently, EPID measurements which presume to take scatter into account are highly sensitive to the identification of these contributions. One example of this susceptibility is the process of calibrating an EPID for use as a gauge of (radiological) thickness, where specific allowance must be made for the effect of phantom-scatter on the intensity of radiation measured through different thicknesses of phantom. This is usually done via a theoretical calculation which assumes that phantom scatter is linearly related to thickness and field-size. We have, however, undertaken a more detailed study of the scattering effects of fields of different dimensions when applied to phantoms of various thicknesses in order to derive scattered-primary ratios (SPRs) directly from simulation results. This allows us to make a more-accurate calibration of the EPID, and to qualify the appositeness of the theoretical SPR calculations. Methods: This study uses a full MC model of the entire linac-phantom-detector system simulated using EGSnrc/BEAMnrc codes. The Elekta linac and EPID are modelled according to specifications from the manufacturer and the intervening phantoms are modelled as rectilinear blocks of water or plastic, with their densities set to a range of physically realistic and unrealistic values. Transmissions through these various phantoms are calculated using the dose detected in the model EPID and used in an evaluation of the field-size-dependence of SPR, in different media, applying a method suggested for experimental systems by Swindell and Evans [1]. These results are compared firstly with SPRs calculated using the theoretical, linear relationship between SPR and irradiated volume, and secondly with SPRs evaluated from our own experimental data. An alternate evaluation of the SPR in each simulated system is also made by modifying the BEAMnrc user code READPHSP, to identify and count those particles in a given plane of the system that have undergone a scattering event. In addition to these simulations, which are designed to closely replicate the experimental setup, we also used MC models to examine the effects of varying the setup in experimentally challenging ways (changing the size of the air gap between the phantom and the EPID, changing the longitudinal position of the EPID itself). Experimental measurements used in this study were made using an Elekta Precise linear accelerator, operating at 6MV, with an Elekta iView GT a-Si EPID. Results and Discussion: 1. Comparison with theory: With the Elekta iView EPID fixed at 160 cm from the photon source, the phantoms, when positioned isocentrically, are located 41 to 55 cm from the surface of the panel. At this geometry, a close but imperfect agreement (differing by up to 5%) can be identified between the results of the simulations and the theoretical calculations. However, this agreement can be totally disrupted by shifting the phantom out of the isocentric position. Evidently, the allowance made for source-phantom-detector geometry by the theoretical expression for SPR is inadequate to describe the effect that phantom proximity can have on measurements made using an (infamously low-energy sensitive) a-Si EPID. 2. Comparison with experiment: For various square field sizes and across the range of phantom thicknesses, there is good agreement between simulation data and experimental measurements of the transmissions and the derived values of the primary intensities. However, the values of SPR obtained through these simulations and measurements seem to be much more sensitive to slight differences between the simulated and real systems, leading to difficulties in producing a simulated system which adequately replicates the experimental data. (For instance, small changes to simulated phantom density make large differences to resulting SPR.) 3. Comparison with direct calculation: By developing a method for directly counting the number scattered particles reaching the detector after passing through the various isocentric phantom thicknesses, we show that the experimental method discussed above is providing a good measure of the actual degree of scattering produced by the phantom. This calculation also permits the analysis of the scattering sources/sinks within the linac and EPID, as well as the phantom and intervening air. Conclusions: This work challenges the assumption that scatter to and within an EPID can be accounted for using a simple, linear model. Simulations discussed here are intended to contribute to a fuller understanding of the contribution of scattered radiation to the EPID images that are used in dosimetry calculations. Acknowledgements: This work is funded by the NHMRC, through a project grant, and supported by the Queensland University of Technology (QUT) and the Royal Brisbane and Women's Hospital, Brisbane, Australia. The authors are also grateful to Elekta for the provision of manufacturing specifications which permitted the detailed simulation of their linear accelerators and amorphous-silicon electronic portal imaging devices. Computational resources and services used in this work were provided by the HPC and Research Support Group, QUT, Brisbane, Australia.

The development of Monte Carlo techniques for the verification of radiotherapy treatments

Introduction: Recent advances in the planning and delivery of radiotherapy treatments have resulted in improvements in the accuracy and precision with which therapeutic radiation can be administered. As the complexity of the treatments increases it becomes more difficult to predict the dose distribution in the patient accurately. Monte Carlo (MC) methods have the potential to improve the accuracy of the dose calculations and are increasingly being recognised as the ‘gold standard’ for predicting dose deposition in the patient [1]. This project has three main aims: 1. To develop tools that enable the transfer of treatment plan information from the treatment planning system (TPS) to a MC dose calculation engine. 2. To develop tools for comparing the 3D dose distributions calculated by the TPS and the MC dose engine. 3. To investigate the radiobiological significance of any errors between the TPS patient dose distribution and the MC dose distribution in terms of Tumour Control Probability (TCP) and Normal Tissue Complication Probabilities (NTCP). The work presented here addresses the first two aims. Methods: (1a) Plan Importing: A database of commissioned accelerator models (Elekta Precise and Varian 2100CD) has been developed for treatment simulations in the MC system (EGSnrc/BEAMnrc). Beam descriptions can be exported from the TPS using the widespread DICOM framework, and the resultant files are parsed with the assistance of a software library (PixelMed Java DICOM Toolkit). The information in these files (such as the monitor units, the jaw positions and gantry orientation) is used to construct a plan-specific accelerator model which allows an accurate simulation of the patient treatment field. (1b) Dose Simulation: The calculation of a dose distribution requires patient CT images which are prepared for the MC simulation using a tool (CTCREATE) packaged with the system. Beam simulation results are converted to absolute dose per- MU using calibration factors recorded during the commissioning process and treatment simulation. These distributions are combined according to the MU meter settings stored in the exported plan to produce an accurate description of the prescribed dose to the patient. (2) Dose Comparison: TPS dose calculations can be obtained using either a DICOM export or by direct retrieval of binary dose files from the file system. Dose difference, gamma evaluation and normalised dose difference algorithms [2] were employed for the comparison of the TPS dose distribution and the MC dose distribution. These implementations are spatial resolution independent and able to interpolate for comparisons. Results and Discussion: The tools successfully produced Monte Carlo input files for a variety of plans exported from the Eclipse (Varian Medical Systems) and Pinnacle (Philips Medical Systems) planning systems: ranging in complexity from a single uniform square field to a five-field step and shoot IMRT treatment. The simulation of collimated beams has been verified geometrically, and validation of dose distributions in a simple body phantom (QUASAR) will follow. The developed dose comparison algorithms have also been tested with controlled dose distribution changes. Conclusion: The capability of the developed code to independently process treatment plans has been demonstrated. A number of limitations exist: only static fields are currently supported (dynamic wedges and dynamic IMRT will require further development), and the process has not been tested for planning systems other than Eclipse and Pinnacle. The tools will be used to independently assess the accuracy of the current treatment planning system dose calculation algorithms for complex treatment deliveries such as IMRT in treatment sites where patient inhomogeneities are expected to be significant. Acknowledgements: Computational resources and services used in this work were provided by the HPC and Research Support Group, Queensland University of Technology, Brisbane, Australia. Pinnacle dose parsing made possible with the help of Paul Reich, North Coast Cancer Institute, North Coast, New South Wales.