Health professionals, their medical interventions and uncertainty : a study focusing on women at midlife

Health professionals face a tension between focusing on the individual and attending to health issues for the population as a whole. This tension is intrinsic to medicine and gives rise to medical uncertainty, which here is explored through accounts of three medical interventions focused on women at midlife: breast screening, hormone replacement therapy and bone densitometry. The accounts come from interviews with UK health professionals using these medical interventions in their daily work. Drawing on the analysis of Fox [(2002). Health and Healing: The public/private divide (pp. 236–253). London: Routledge] we distinguish three aspects of medical uncertainty and explore each one of them in relation to one of the interventions. First is uncertainty about the balance between the individual and distributive ethic of medicine, explored in relation to breast screening. Second is the dilemma faced by health professionals when using medicial evidence generated through studies of populations and applying this to individuals. We explore this dilemma for hormone replacement therapy. Thirdly there is uncertainty because of the lack of a conceptual framework for understanding how new micro knowledge, such as human genetic information, can be combined with knowledge of other biological and social dimensions of health. The accounts from the bone denistometry clinic indicate the beginnings of an understanding of the need for such a framework, which would acknowledge complexity, recognising that factors from many different levels of analysis, from heredity through to social factors, interact with each other and influence the individual and their health. However, our analysis suggests biomedicine continues to be dominated by an individualised, context free, concept of health and health risk with individuals alone responsible for their own health and for the health of the population. This may continue to dominate how we perceive responsibilities for health until we establish a conceptual framework that recognises the complex interaction of many factors at macro and micro level affecting health

Inter-observer agreement of canine and feline paroxysmal event semiology and classification by veterinary neurology specialists and non-specialists

Background: Advances in mobile technology mean vets are now commonly presented with videos of paroxysmal events by clients, but the consistency of the interpretation of these videos has not been investigated. The objective of this study was to investigate the level of agreement between vets (both neurology specialists and non-specialists) on the description and classification of videos depicting paroxysmal events, without knowing any results of diagnostic workup. An online questionnaire study was conducted, where participants watched 100 videos of dogs and cats exhibiting paroxysmal events and answered questions regarding: epileptic seizure presence (yes/ no), seizure type, consciousness status, and the presence of motor, autonomic and neurobehavioural signs. Agreement statistics (percentage agreement and kappa) calculated for each variable, with prevalence indices calculated to aid their interpretation. Results: Only a fair level of agreement (kappa = 0.40) was found for epileptic seizure presence. Overall agreement of seizure type was moderate (kappa = 0.44), with primary generalised seizures showing the highest level of agreement (kappa = 0.60), and focal the lowest (kappa = 0.31). Fair agreement was found for consciousness status and the presence of autonomic signs (kappa = 0.21-0.40), but poor agreement for neurobehavioral signs (kappa = 0.16). Agreement for motor signs ranged from poor (kappa = <= 0.20) to moderate (kappa = 0.41-0.60). Differences between specialists and non-specialists were identified. Conclusions: The relatively low levels of agreement described here highlight the need for further discussions between neurology experts regarding classifying and describing epileptic seizures, and additional training of non-specialists to facilitate accurate diagnosis. There is a need for diagnostic tools (e.g. electroencephalogram) able to differentiate between epileptic and non-epileptic paroxysms

Modelling of a Gas Cap Gas Lift System

Imperial Users onl

Providing decision support for the condition-based maintenance of circuit breakers through data mining of trip coil current signatures

The focus of this paper centers on the condition assessment of 11kV-33kV distribution circuit breakers from the analysis of their trip coil current signatures captured using an innovative condition monitoring technology developed by others. Using available expert knowledge in conjunction with a structured process of data mining, thresholds associated with features representing each stage of a circuit breaker's operation may be defined and used to characterize varying states of circuit breaker condition. Knowledge and understanding of satisfactory and unsatisfactory breaker condition can be gained and made explicit from the analysis of captured trip signature data and subsequently used to form the basis of condition assessment and diagnostic rules implemented in a decision support system, used to inform condition-based decisions affecting circuit breaker maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of SP Power System's in-service circuit breakers. This knowledge then forms the basis of a decision support system for the condition assessment of these circuit breakers during routine trip testing

The Future of the Internet III

Presents survey results on technology experts' predictions on the Internet's social, political, and economic impact as of 2020, including its effects on integrity and tolerance, intellectual property law, and the division between personal and work lives

Conditional Similarity Networks

What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.Comment: CVPR 201