A review of clinical decision-making: Models and current research

Aims and objectives: The aim of this paper was to review the current literature with respect to clinical decision-making models and the educational application of models to clinical practice. This was achieved by exploring the function and related research of the three available models of clinical decision making: information processing model, the intuitive-humanist model and the clinical decision making model. Background: Clinical decision-making is a unique process that involves the interplay between knowledge of pre-existing pathological conditions, explicit patient information, nursing care and experiential learning. Historically, two models of clinical decision making are recognised from the literature; the information processing model and the intuitive-humanist model. The usefulness and application of both models has been examined in relation the provision of nursing care and care related outcomes. More recently a third model of clinical decision making has been proposed. This new multidimensional model contains elements of the information processing model but also examines patient specific elements that are necessary for cue and pattern recognition. Design: Literature review Methods: Evaluation of the literature generated from MEDLINE, CINAHL, OVID, PUBMED and EBESCO systems and the Internet from 1980 – November 2005

Cardiovascular co-medication among users of antiobesity drugs: a population-based study

Aim The purpose of this study was to investigate to what extent patients using prescription antiobesity drugs (orlistat, sibutramine and rimonabant) used cardiovascular and antidiabetic drugs. An additional aim was to investigate whether such co-medication differed according to gender, age and amount of antiobesity drugs used. Method Data were retrieved from the Norwegian Prescription Database (NorPD). All patients who had an antiobesity drug (ATC code A08A) dispensed from a Norwegian pharmacy between January 2004 and December 2007 were included in the study. Results During the 4-year study period 83,717 patients had antiobesity drugs dispensed. One in three patients using antiobesity drugs had at least on one occasion used a cardiovascular and/or an antidiabetic drug concomitantly. A significantly higher percentage of men used antihypertensives (40.4 vs. 27.2%, P < 0.0005), lipid modifying agents (24.4 vs. 11.9%, P < 0.0005) and drugs used in diabetes (12.7 vs. 6.4%, P < 0.0005) concomitantly with antiobesity drugs when compared to women. The percentage of patients who had concomitant drug use increased markedly with age. One in four patients had antiobesity drugs dispensed only once during the period 2004–2007. Conclusion Use of cardiovascular and antidiabetic drugs among patients using antiobesity drugs was extensive, especially among men and elderly patients. Overall, there was a high degree of polypharmacy among users of antiobesity drugs. Also, many patients dispensed antiobesity drugs in amounts that indicated use less than the recommended daily dose, and many dispensed antiobesity drugs only once. When prescribing antiobesity drugs to patients the potential benefits of antiobesity drugs should be considered in relation to the patients other chronic diseases and to the total complexity of the patients drug regimen

Resurrection of an ancestral 5S rRNA

<p>Abstract</p> <p>Background</p> <p>In addition to providing phylogenetic relationships, tree making procedures such as parsimony and maximum likelihood can make specific predictions of actual historical sequences. Resurrection of such sequences can be used to understand early events in evolution. In the case of RNA, the nature of parsimony is such that when applied to multiple RNA sequences it typically predicts ancestral sequences that satisfy the base pairing constraints associated with secondary structure. The case for such sequences being actual ancestors is greatly improved, if they can be shown to be biologically functional.</p> <p>Results</p> <p>A unique common ancestral sequence of 28 <it>Vibrio </it>5S ribosomal RNA sequences predicted by parsimony was resurrected and found to be functional in the context of the <it>E. coli </it>cellular environment. The functionality of various point variants and intermediates that were constructed as part of the resurrection were examined in detail. When separately introduced the changes at single stranded positions and individual double variants at base-paired positions were also viable. An additional double variant was examined at a different base-paired position and it was also valid.</p> <p>Conclusions</p> <p>The results show that at least in the case of the 5S rRNAs considered here, ancestors predicted by parsimony are likely to be realistic when the prediction is not overly influenced by single outliers. It is especially noteworthy that the phenotype of the predicted ancestors could be anticipated as a cumulative consequence of the phenotypes of the individual variants that comprised them. Thus, point mutation data is potentially useful in evaluating the reasonableness of ancestral sequences predicted by parsimony or other methods. The results also suggest that in the absence of significant tertiary structure constraints double variants that preserve pairing in stem regions will typically be accepted. Overall, the results suggest that it will be feasible to resurrect additional meaningful 5S rRNA ancestors as well as ancestral sequences of many different types of RNA.</p

Exploring Norms in Agile Software Teams

The majority of software developers work in teams and are thus influenced by team norms. Norms are shared expectations of how to behave and regulate the interaction between team members. Our aim of this study is to gain more knowledge about team norms in software teams and to increase the understanding of how norms influence teamwork in agile software development projects. We conducted a study of norms in four agile teams located in Norway and Malaysia. The analysis of 22 interviews revealed that we could extract a varied set of both injunctive and descriptive norms. Our results suggest that team norms have an important role in enabling team performance.acceptedVersio

Latitudinal Gradients in Degradation of Marine Dissolved Organic Carbon

Heterotrophic microbial communities cycle nearly half of net primary productivity in the ocean, and play a particularly important role in transformations of dissolved organic carbon (DOC). The specific means by which these communities mediate the transformations of organic carbon are largely unknown, since the vast majority of marine bacteria have not been isolated in culture, and most measurements of DOC degradation rates have focused on uptake and metabolism of either bulk DOC or of simple model compounds (e.g. specific amino acids or sugars). Genomic investigations provide information about the potential capabilities of organisms and communities but not the extent to which such potential is expressed. We tested directly the capabilities of heterotrophic microbial communities in surface ocean waters at 32 stations spanning latitudes from 76°S to 79°N to hydrolyze a range of high molecular weight organic substrates and thereby initiate organic matter degradation. These data demonstrate the existence of a latitudinal gradient in the range of complex substrates available to heterotrophic microbial communities, paralleling the global gradient in bacterial species richness. As changing climate increasingly affects the marine environment, changes in the spectrum of substrates accessible by microbial communities may lead to shifts in the location and rate at which marine DOC is respired. Since the inventory of DOC in the ocean is comparable in magnitude to the atmospheric CO2 reservoir, such a change could profoundly affect the global carbon cycle

Assessment and optimisation of normalisation methods for dual-colour antibody microarrays

<p>Abstract</p> <p>Background</p> <p>Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.</p> <p>Results</p> <p>We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power.</p> <p>Conclusions</p> <p>The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.</p

Does inequality erode generalized trust? Evidence from Romanian youths

Generalized trust is a critical component of liberal democratic citizenship. We evaluate the extent to which exposure to socioeconomic inequality erodes trust among Romanian youths. Using national survey data of Romanian eighth-grade and high school students, we evaluate this effect as a product of socioeconomic diversity within the classroom, controlling for the social status of the students as well as socioeconomic inequality within the community where the school is located. Our analysis shows that generalized trust is higher for students in higher grades. However, despite this maturing effect, students exposed to greater levels of socioeconomic diversity have significantly lower levels of trust. The effect is particularly acute for students in the ninth grade. This finding holds when controlling for socioeconomic diversity and polarization in the community. The result reinforces the idea that generalized trust develops early in one’s life and is quite stable, although a major life transformation, such as entering high school, may alter trust depending on the social context

International Standard ISO 9001–A Soft Computing View

In order to add value to ISO 9001, a Quality Management Systems that assess, measure, documents, improves, and certify processes to increase productivity, i.e., that transforms business at any level. On the one hand, this work focuses on the development of a decision support system, which will allow companies to be able to meet the needs of customers by fulfilling requirements that reflect either the effectiveness or the non-effectiveness of an organization. On the other hand, many approaches for knowledge representation and reasoning have been proposed using Logic Programming (LP), namely in the area of Model Theory or Proof Theory. In this work it is followed the proof theoretical approach in terms of an extension to the LP language to knowledge representation and reasoning. The computational framework is centered on Artificial Neural Networks to evaluate customer’s satisfaction and the degree of confidence that one has on such a happening

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data

<p>Abstract</p> <p>Background</p> <p>Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.</p> <p>We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.</p> <p>Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution.</p> <p>Results</p> <p>Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (<it>L</it>₁) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.</p> <p>Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations.</p> <p>Conclusions</p> <p>The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.</p> <p>The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'.</p> <p>We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.</p