Cognitive-behavioral therapy for obsessive-compulsive disorder: access to treatment, prediction of long-term outcome with neuroimaging.

This article reviews issues related to a major challenge to the field for obsessive-compulsive disorder (OCD): improving access to cognitive-behavioral therapy (CBT). Patient-related barriers to access include the stigma of OCD and reluctance to take on the demands of CBT. Patient-external factors include the shortage of trained CBT therapists and the high costs of CBT. The second half of the review focuses on one partial, yet plausible aid to improve access - prediction of long-term response to CBT, particularly using neuroimaging methods. Recent pilot data are presented revealing a potential for pretreatment resting-state functional magnetic resonance imaging and magnetic resonance spectroscopy of the brain to forecast OCD symptom severity up to 1 year after completing CBT

The Consequences of Non-Classical Measurement Error for Distributional Analysis

This paper analyzes the consequences of non-classical measurement error for distributional analysis. We show that for a popular set of distributions negative correlation between the measurement error (u) and the true value (y) may reduce the bias in the estimated distribution at every value of y. For other distributions the impact of non-classical measurement di¤ers throughout the support of the distribution. We illustrate the practical importance of these results using models of unemployment duration and income.Distribution functions,Non-classical measurement error,

A geometric and stochastic proof of the twist point theorem

In this paper we will give a proof of the McMillan twist point theorem using geometry, potential theory and Ito's formula but not the Riemann mapping theorem

Longevity and mortality of owned dogs in England

Improved understanding of longevity represents a significant welfare opportunity for the domestic dog, given its unparalleled morphological diversity. Epidemiological research using electronic patient records (EPRs) collected from primary veterinary practices overcomes many inherent limitations of referral clinic, owner questionnaire and pet insurance data. Clinical health data from 102,609 owned dogs attending first opinion veterinary practices (n = 86) in central and southeast England were analysed, focusing on 5095 confirmed deaths. Of deceased dogs with information available, 3961 (77.9%) were purebred, 2386 (47.0%) were female, 2528 (49.8%) were neutered and 1105 (21.7%) were insured. The overall median longevity was 12.0 years (IQR 8.9–14.2). The longest-lived breeds were the Miniature poodle, Bearded collie, Border collie and Miniature dachshund, while the shortest-lived were the Dogue de Bordeaux and Great Dane. The most frequently attributed causes of death were neoplastic, musculoskeletal and neurological disorders. The results of multivariable modelling indicated that longevity in crossbred dogs exceeded purebred dogs by 1.2 years (95% confidence interval 0.9–1.4; P < 0.001) and that increasing bodyweight was negatively correlated with longevity. The current findings highlight major breed differences for longevity and support the concept of hybrid vigour in dogs

Approaches to canine health surveillance

Effective canine health surveillance systems can be used to monitor disease in the general population, prioritise disorders for strategic control and focus clinical research, and to evaluate the success of these measures. The key attributes for optimal data collection systems that support canine disease surveillance are representativeness of the general population, validity of disorder data and sustainability. Limitations in these areas present as selection bias, misclassification bias and discontinuation of the system respectively. Canine health data sources are reviewed to identify their strengths and weaknesses for supporting effective canine health surveillance. Insurance data benefit from large and well-defined denominator populations but are limited by selection bias relating to the clinical events claimed and animals covered. Veterinary referral clinical data offer good reliability for diagnoses but are limited by referral bias for the disorders and animals included. Primary-care practice data have the advantage of excellent representation of the general dog population and recording at the point of care by veterinary professionals but may encounter misclassification problems and technical difficulties related to management and analysis of large datasets. Questionnaire surveys offer speed and low cost but may suffer from low response rates, poor data validation, recall bias and ill-defined denominator population information. Canine health scheme data benefit from well-characterised disorder and animal data but reflect selection bias during the voluntary submissions process. Formal UK passive surveillance systems are limited by chronic under-reporting and selection bias. It is concluded that active collection systems using secondary health data provide the optimal resource for canine health surveillance

Towards an expanded model of litigation

Introduction: The call for contributions for this workshop describes the important new challenges for the legal search community this domain brings. Rather than just understanding the challenges this domain poses in terms of their technical properties, we would like to suggest that understanding these challenges as socio-technical challenges will be important. That is, as well as calling for research on a technical level to address these challenges we are also calling for work to understand the social practices of those involved in e-discovery (ED) and related legal work. A particularly interesting feature of this field is that it is likely that search technologies will (at least semi-)automate responsiveness review in the relatively near term and this will change the way that the work is organised and done in many ways – offering new possibilities for new ways of organising the work. As well as designing those technologies for automating responsiveness review we need to be envisioning how the work will be done in the future, how these technologies will impact the organisation of the case and so on. In this position paper we therefore outline the importance of understanding the wider social context of ED when designing tools and technologies to support and change the work. We would like to reinforce and expand on Conrad’s call for IR researchers to understand just what ED entails [2], include the stages that come both before and after core retrieval activities. The importance of considering the social aspects of work in the design of the technology has been established for some time. Ushering in this ‘turn to the social,’ and focusing on interface design, Gentner and Grudin [4] described how the GUI has already changed from an interface for engineers, representing the engineering model of the machine to one that supported single ‘everyman’ users (based on ideas from psychology). From then onwards the interface has evolved to support groups of users, taking into account the social and organisational contexts of use. This has particular resonance for the design of ED technologies: during ED in particular and the wider legal process there are often many lawyers involved – reviewing documents, determining issues, etc. Even if the way that their work is organised currently is not seen as collaborative in the traditional sense – with individual lawyers working on individual document sets to review them - their work needs to be coordinated and it seems likely that their work could be enhanced by, for example, knowledge of what their colleagues had found, how the case was shaping up, new key terms and facts turned up and so on. Work is often modelled for the purposes of design using process models, but this misses out on the richness and variety actually found when one examines how the work is carried out [3]. Technologies which strictly enforce the process models can often hinder the work, or end up being worked around as was the case with workflow systems since people interpret processes very flexibly to get the work done ([1], [3]). Other studies in other fields have found similar problems when systems are designed on for example cognitive models of how the work is done; they often do not take into account the situated nature of the work and thus they can be very difficult to use [5]. We believe, like [2], that a clear understanding of the social practices of ED is vital for the creation of high-quality, meaningful tools and technologies. We furthermore propose that work practice studies, to be used in combination with other methods, are a central part of getting the detailed understanding of the work practices central to designing useful and intelligent tools. Work practice studies would involve ethnographies, consisting primarily of observation, undertaken of practitioners engaging in the work of ED