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Distribution by Scientific Domains

Medical Sciences	50%

Selected Abstracts

Predicting pressure ulcer risk: a multifactorial approach to assess risk factors in a large university hospital population

JOURNAL OF CLINICAL NURSING, Issue 1 2009
Michael Nonnemacher
Aims., The purpose of this study was: (1) to determine the combination of risk factors which best predicts the risk of developing pressure ulcers among inpatients in an acute care university hospital; (2) to determine the appropriate weight for each risk factor; and (3) to derive a concise and easy-to-use risk assessment tool for daily use by nursing staff. Background., Efficient application of preventive measures against pressure ulcers requires the identification of patients at risk. Adequate risk assessment tools are still needed because the predictive value of existing tools is sometimes unsatisfactory. Design., Survey. Methods., A sample of 34,238 cases admitted to Essen University Clinics from April 2003 and discharged up to and including March 2004, was enrolled into the study. Nursing staff recorded data on pressure ulcer status and potential risk factors on admission. Predictors were identified and weighted by multivariate logistic regression. We derived a risk assessment scale from the final logistic regression model by assigning point values to each predictor according to its individual weight. Results., The period prevalence rate of pressure ulcers was 1·8% (625 cases). The analysis identified 12 predictors for developing pressure ulcers. With the optimum cut-off point sensitivity and specificity were 83·4 and 83·1%, respectively, with a positive predictive value of 8·4% and a negative predictive value of 99·6%. The diagnostic probabilities of the derived scale were similar to those of the original regression model. Conclusions., The predictors mostly correspond to those used in established scales, although the use of weighted factors is a partly novel approach. Both the final regression model and the derived scale show good prognostic validity. Relevance to clinical practice., The derived risk assessment scale is an easy-to-understand, easy-to-use tool with good prognostic validity and can assist in effective application of preventive measures against pressure ulcer. [source]

Estimating food intakes in Australia: validation of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) food frequency questionnaire against weighed dietary intakes

JOURNAL OF HUMAN NUTRITION & DIETETICS, Issue 6 2009
C. Lassale
Abstract Background:, There is a dearth of knowledge about the foods that Australian adults eat and a need for a flexible, easy-to-use tool that can estimate usual dietary intakes. The present study was to validate a commonly used Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) food-frequency questionnaire (C-FFQ) against two 4-day weighed food records (WFR), as the reference method. Methods:, The C-FFQ, as the test item, was administrated before the WFR. Two 4-day WFR were administrated 4 weeks apart. Under-reporting was established using specific cut-off limits and estimated basal metabolic rate. Seventy-four women, aged 31,60 years, were enrolled from a free-living community setting. Results:, After exclusion for under-reporting, the final sample comprised 62 individuals. Correlations between protein intake from the WFR and urinary urea were significant. Overall agreement between FFQ and WFR was shown by ,levels of agreement' (LOA) and least products regressions. There was presence of fixed and proportional bias for almost half the nutrients, including energy, protein, fat and carbohydrates. For most of the nutrients that did not present bias, the LOA were 50,200%. Agreement was demonstrated for percentage dietary energy protein and fat; carbohydrate; and absolute amounts of thiamine, riboflavin, magnesium and iron. However, relative intake agreement was fair to moderate, with approximately 70% of (selected) nutrients exact or within ±1 quintile difference. Conclusion:, The C-FFQ is reasonable at measuring percentage energy from macronutrients and some micronutrients, and comprises a valuable tool for ranking intakes by quintiles; however, it is poor at measuring many absolute nutrient intakes relative to WFR. [source]

Application of support vector regression for developing soft sensors for nonlinear processes,

THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 5 2010
Saneej B. Chitralekha
Abstract The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input,output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high-dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy-to-use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady-state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR-based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least-square-based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear-ARX model-based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input,output data. Le domaine du développement des capteurs logiciels a récemment gagné en importance avec la création d'outils de calcul efficaces et facilement utilisables à cette fin. L'idée de base est de convertir l'information obtenue dans les données d'entrée et de sortie recueillies à partir du processus dans un modèle mathématique. Un tel modèle mathématique peut servir de solution de rechange économique pour les capteurs matériels. L'outil de régression par machine à vecteur de support (RMVS) constitue un outil de calcul qui a récemment été l'objet de beaucoup d'attention dans la littérature des systèmes d'identification, surtout en raison de ses succès dans la création de modèles de boîte noire non linéaires. Dans ce travail, nous démontrons l'application de la RMVS comme outil efficace et facile à utiliser pour la création de capteurs logiciels pour les procédés non linéaires. Dans une étude de cas industrielle, nous illustrons le développement d'un capteur logiciel à indice de fluidité à état permanent pour un processus d'extrusion du polymère d'acétate de vinyle-éthylène à l'échelle industrielle en utilisant la RMVS. Le capteur logiciel fondé sur la RMVS, valide sur une vaste gamme d'indices de fluidité, a surclassé le capteur logiciel fondé sur les moindres carrés non linéaires existant en matière d'erreurs de prédiction plus faibles. Dans les deux autres études de cas, nous démontrons l'application de la RMVS pour la création de capteurs logiciels sous la forme de modèles dynamiques pour deux procédés non linéaires: un processus de neutralisation du pH simulé et un processus d'extrusion de polymère à deux vis à l'échelle laboratoire. Une procédure heuristique est proposée pour la création d'un capteur logiciel fondé sur un modèle ARX non linéaire dynamique en utilisant la RMVS, dans lequel on atteint automatiquement le délai optimal et les ordres en utilisant les données d'entrée et de sortie. [source]

Two scenarios for productive learning environments in the workplace

BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, Issue 2 2002
Betty Collis
Productive learning is defined as learning that can be reused, in application to new problem situations in an organisation or for assimilation and reflection in structured learning situations such as courses. An important but underexploited form of productive learning relates to the capture and reuse of the tacit knowledge of members of an organisation. Two approaches for this reuse of tacit knowledge are discussed, along with instructional strategies and technologies to support the knowledge capture and reuse process within each of the approaches. In one of the illustrated approaches the emphasis is on how those in mentor or supervisor positions can more systematically support the diffusion of their own tacit knowledge to those of their mentees and in the process create new knowledge for reuse in other situations. In the second illustration, a change in orientation from knowledge transfer to knowledge creation and sharing in the formal training programmes of the organisation is the focus. An underlying database as well as easy-to-use tools for resource entry and indexing are key elements in facilitating the reuse of experience-based resources within and across both informal and formal learning. [source]