Expanded review criteria : the case of nonpharmacological interventions in dementia

This paper challenges the assumptions underlying many reviews and offers alternative criteria for examining evidence for nonpharmacological interventions. We evaluated 27 reviews examining interventions for persons with dementia as they relate to the issues of selection based on randomized controlled trial (RCT) design. Reviews were described by type of intervention, level of cognitive function, and criteria for inclusion. Of the 27 reviews, 46% required RCTs for inclusion and most had stringent inclusion criteria. This resulted in poor utilization of the literature and low ecological validity. Eliminating most of the available data poses a critical problem to clinical and research development. Studies meeting strict methodological criteria may not generalize to the greater population or may exclude sub-populations and interventions. Limitations of double-blind RCTs and potential design solutions are set forth based on appropriate populations, problems, interventions, and settings characteristics.

Improving criteria and feedback in student assessment in law

Spurred on by both the 1987 Pearce Report1 and the general changes to higher education spawned by the “Dawkins revolution” from 1988, there has been much critical self-evaluation leading to profound improvements to the quality of teaching in Australian law schools.2 Despite the changes there are still areas of general law teaching practice which have lagged behind recent developments in our understanding of what constitutes high quality teaching. One such area is assessment criteria and feedback. The project Improving Feedback in Student Assessment in Law is an attempt to remedy this. It aims to produce a manual containing key principles for the design of assessment and the provision of feedback, with practical yet flexible ideas and illustrations which law teachers may adopt or modify. Most of the examples have been developed by teachers at the University of Melbourne Law School. The project was supported in 1996 by a Committee for the Advancement of University Teaching grant and the manual will be published late in 1997.3 This note summarises the core principles which are elaborated further in the manual.

eHealth and the modern society

The integration of Information and Communication Technologies (ICT) into healthcare processes “eHealth” is driving enormous change in healthcare delivery and productivity. The transformations empower patients and present opportunities for new synergies between healthcare professionals, clinical decision makers, policy makers and educators. Technologies that are directly driving changes include Tele-medicine, Electronic health records (EHR), Standards to ensure computer systems inter-operate, Decision support systems, Data mining and easy access to medical information. This workshop provides an introduction to key informatics initiatives in eHealth using real examples and suggests how applications can be applied to modern society.

Evaluation of features for leaf classification in challenging conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-theart performance with an average accuracy of 97:3%�0:6% compared to traditional features which obtain an average accuracy of 91:2%�1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5:7% for all of the evaluated condition variations.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

Lighting invariant urban street classification

In this paper we propose the hybrid use of illuminant invariant and RGB images to perform image classification of urban scenes despite challenging variation in lighting conditions. Coping with lighting change (and the shadows thereby invoked) is a non-negotiable requirement for long term autonomy using vision. One aspect of this is the ability to reliably classify scene components in the presence of marked and often sudden changes in lighting. This is the focus of this paper. Posed with the task of classifying all parts in a scene from a full colour image, we propose that lighting invariant transforms can reduce the variability of the scene, resulting in a more reliable classification. We leverage the ideas of “data transfer” for classification, beginning with full colour images for obtaining candidate scene-level matches using global image descriptors. This is commonly followed by superpixellevel matching with local features. However, we show that if the RGB images are subjected to an illuminant invariant transform before computing the superpixel-level features, classification is significantly more robust to scene illumination effects. The approach is evaluated using three datasets. The first being our own dataset and the second being the KITTI dataset using manually generated ground truth for quantitative analysis. We qualitatively evaluate the method on a third custom dataset over a 750m trajectory.

Male sex work and society

This collection explores male sex work from an array of perspectives and disciplines. It aims to help enrich the ways in which we view both male sex work as a field of commerce and male sex workers themselves. Leading contributors examine the field both historically and cross-culturally from fields including public health, sociology, psychology, social services, history, filmography, economics, mental health, criminal justice, geography, and migration studies, and more. Synthesizing introductions by the editors help the reader understand the implications of the findings and conclusions for scholars, practitioners, students, and members of the interested/concerned public.

A systematic review of prescribing criteria to evaluate appropriateness of medications in frail older people

This study systematically reviews the published literature regarding inappropriate prescribing in frail individuals aged at least 65 years. Twenty-five of 466 identified studies met the inclusion criteria. All papers measured some surrogate indicators of frailty, such as performance-based tests, cognitive function and functional dependency. Beers criteria were used in 20 studies (74%) to evaluate inappropriate medication use and 36% (9/25) studies used more than one criterion. The prevalence of inappropriate medications ranged widely from 11 to 92%. Only a few studies reported the relationship between potentially inappropriate medication use and surrogate measures of frailty. These diverse findings indicate the need for a standardized measure for assessing appropriateness of medication in frail older individuals. Prescribing tools should address both medication and patient-related factors such as life expectancy and functional status to minimize inappropriate prescribing in frail individuals.

Navigating uncertainty in Australia : fostering informed citizenship through teaching controversial issues with pre-service teachers

Australia is a multicultural immigrant society created by public policy and direct state action over a period of two hundred years. It is now one of the world’s most diverse societies. However, like many nations, Australia faces challenges to managing ‘unauthorized arrivals’ who claim to be refugees. The issue of how to deal with unauthorized arrivals is controversial and highly emotive as it challenges public policy and government capacity to manage the multicultural ‘mix’ of Australia’s population. It also raises questions about border security. Given that it is impossible to discern beforehand who is a ‘proper’ refugee and who is not, claims to refugee status by unauthorised arrivals in Australia need to be tested against international convention criteria devised by the United Nations High Commissioner for Refugees (UNHCR). There are no simple solutions to controversial questions such as how and where should unauthorised arrivals, and the children accompanying them, be housed whilst their claims are investigated? Moreover, as this issue continues to prompt division and heated debate in Australian society, teachers new to the profession are often reluctant to explore it in the classroom. However, there are opportunities in national and state curriculum documents for the values dimensions of curriculum inquiries into controversial issues such as this to be addressed. For example, the most recent national statement on the goals for schooling in Australia, the Melbourne Declaration (MCEETYA, 2008), makes clear that Australian students need to be prepared for the challenges of the 21st century and to develop the capacity for innovation and complex problem-solving. The Melbourne Declaration informs the first national curriculum to be implemented in the Australian states and territories, and all other national and state initiatives. Its focus on developing active and informed citizens who can contribute to a socially cohesive society implies a capacity to deal with a range of issues associated with cultural diversity, This chapter explores the ways in which pre-service and early career teachers in one Australian state reflect upon curriculum opportunities to address controversial issues in the social sciences and history classroom. As part of their pre-service education, all the participants in this study completed a final year social science curriculum method unit that embedded a range of controversial issues, including the placement of children in Australian Immigration Detention Centres (IDCs), for investigation. By drawing from interviews and focus groups conducted with different cohorts of pre-service teachers in their final year of university study and beginning years of teaching, this chapter analyses the range of perceptions about how controversial issues can be examined in the secondary classroom as part of fostering informed citizenship. The discussion and analysis of the qualitative data in this study makes no claims for the representativeness of its findings, rather, a range of beginner teacher insights into a complex and important facet of teaching in a period of change and uncertainty is offered.

Classification of echolocation calls from 14 species of bat by Support Vector Machines and Ensembles of Neural Networks

Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance.

Echolocation calls of the long tailed bat: a quantitative analysis of types of calls

The echolocation calls of long-tailed bats (Chalinolobus tuberculatus) were recorded in the Eglinton Valley, Fjordland, New Zealand, and digitized for analysis with the signal-processing software. Univariate and multivariate analyses of measure features facilitated a quantitative classification of the calls. Cluster analysis was used to categorize calls into two groups equating to search and terminal buzz calls described qualitatively for other species. When moving from search to terminal phases, the calls decrease in bandwidth, maximum and minimum frequency of call, and duration. Search calls begin with a steep-downward FM sweep followed by a short, less-modulated component. Buzz calls are FM sweeps. Although not found quantitatively, a broad pre-buzz group of calls also was identified. Ambiguity analysis of calls from the three groups shows that search-phrase calls are well suited to resolving the velocity of targets, and hence, identifying moving targets in a stationary clutter. Pre-buzz and buzz calls are better suited to resolving range, a feature that may aid the bats in capture of evasive prey after it has been identified.

The conservation status of New Zealand bats, 2009

The New Zealand Threat Classification System (NZTCS) is a national system used to assess the risk of extinction faced by New Zealand plants, animals and fungi. The system is specifically designed to be relevant to New Zealand's unusual ecological and geographic conditions. We undertook a re-evaluation of the status of seven bat taxa based on our knowledge of New Zealand bats using revised NZTCS criteria. Five taxa were listed as Threatened or At Risk: one as Nationally Critical (long-tailed bat Chalinolobus tuberculatus ‘South Island’), one as Nationally Endangered (southern lesser short-tailed bat Mystacina tuberculata tuberculata), two as Nationally Vulnerable (long-tailed bat ‘North Island’ and northern lesser short-tailed bat M. t. aupourica) and one as Declining (central lesser short-tailed bat M. t. rhyacobia). One taxon was assessed as Data Deficient (greater short-tailed bat M. robusta) and one (little red flying fox Pteropus scapulatus) as Vagrant. We suspect declines result primarily from predation and competition from introduced mammals, habitat degradation, and disturbance.

The contribution of major depression to the global burden of ischemic heart disease : a comparative risk assessment

Background Cardiovascular disease and mental health both hold enormous public health importance, both ranking highly in results of the recent Global Burden of Disease Study 2010 (GBD 2010). For the first time, the GBD 2010 has systematically and quantitatively assessed major depression as an independent risk factor for the development of ischemic heart disease (IHD) using comparative risk assessment methodology. Methods A pooled relative risk (RR) was calculated from studies identified through a systematic review with strict inclusion criteria designed to provide evidence of independent risk factor status. Accepted case definitions of depression include diagnosis by a clinician or by non-clinician raters adhering to Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD) classifications. We therefore refer to the exposure in this paper as major depression as opposed to the DSM-IV category of major depressive disorder (MDD). The population attributable fraction (PAF) was calculated using the pooled RR estimate. Attributable burden was calculated by multiplying the PAF by the underlying burden of IHD estimated as part of GBD 2010. Results The pooled relative risk of developing IHD in those with major depression was 1.56 (95% CI 1.30 to 1.87). Globally there were almost 4 million estimated IHD disability-adjusted life years (DALYs), which can be attributed to major depression in 2010; 3.5 million years of life lost and 250,000 years of life lived with a disability. These findings highlight a previously underestimated mortality component of the burden of major depression. As a proportion of overall IHD burden, 2.95% (95% CI 1.48 to 4.46%) of IHD DALYs were estimated to be attributable to MDD in 2010. Eastern Europe and North Africa/Middle East demonstrate the highest proportion with Asia Pacific, high income representing the lowest. Conclusions The present work comprises the most robust systematic review of its kind to date. The key finding that major depression may be responsible for approximately 3% of global IHD DALYs warrants assessment for depression in patients at high risk of developing IHD or at risk of a repeat IHD event.