Maastricht University

About: Maastricht University is a education organization based out in Maastricht, Limburg, Netherlands. It is known for research contribution in the topics: Population & Health care. The organization has 19263 authors who have published 53291 publications receiving 2266866 citations. The organization is also known as: Universiteit Maastricht & UM.

Assessing the Quality of Decision Support Technologies Using the International Patient Decision Aid Standards instrument (IPDASi)

Abstract: Objectives To describe the development, validation and inter-rater reliability of an instrument to measure the quality of patient decision support technologies (decision aids). Design Scale development study, involving construct, item and scale development, validation and reliability testing. Setting There has been increasing use of decision support technologies – adjuncts to the discussions clinicians have with patients about difficult decisions. A global interest in developing these interventions exists among both for-profit and not-for-profit organisations. It is therefore essential to have internationally accepted standards to assess the quality of their development, process, content, potential bias and method of field testing and evaluation. Methods Scale development study, involving construct, item and scale development, validation and reliability testing. Participants Twenty-five researcher-members of the International Patient Decision Aid Standards Collaboration worked together to develop the instrument (IPDASi). In the fourth Stage (reliability study), eight raters assessed thirty randomly selected decision support technologies. Results IPDASi measures quality in 10 dimensions, using 47 items, and provides an overall quality score (scaled from 0 to 100) for each intervention. Overall IPDASi scores ranged from 33 to 82 across the decision support technologies sampled (n = 30), enabling discrimination. The inter-rater intraclass correlation for the overall quality score was 0.80. Correlations of dimension scores with the overall score were all positive (0.31 to 0.68). Cronbach's alpha values for the 8 raters ranged from 0.72 to 0.93. Cronbach's alphas based on the dimension means ranged from 0.50 to 0.81, indicating that the dimensions, although well correlated, measure different aspects of decision support technology quality. A short version (19 items) was also developed that had very similar mean scores to IPDASi and high correlation between short score and overall score 0.87 (CI 0.79 to 0.92). Conclusions This work demonstrates that IPDASi has the ability to assess the quality of decision support technologies. The existing IPDASi provides an assessment of the quality of a DST's components and will be used as a tool to provide formative advice to DSTs developers and summative assessments for those who want to compare their tools against an existing benchmark.

Wall shear stress--an important determinant of endothelial cell function and structure--in the arterial system in vivo. Discrepancies with theory.

Abstract: It has been well established that wall shear stress is an important determinant of endothelial cell function and gene expression as well as of its structure. There is increasing evidence that low wall shear stress, as present in artery bifurcations opposite to the flow divider where atherosclerotic lesions preferentially originate, expresses an atherogenic endothelial gene profile. Besides, wall shear stress regulates arterial diameter by modifying the release of vasoactive mediators by endothelial cells. Most of the studies on the influence of wall shear stress on endothelial cell function and structure have been performed in vitro, generally exposing endothelial cells from different vascular regions to an average wall shear stress level calculated according to Poiseuille's law, which does not hold for the in vivo situation, assuming wall shear stress to be constant along the arterial tree. Also in vivo wall shear stress has been determined based upon theory, assuming the velocity profile in arteries to be parabolic, which is generally not the case. Wall shear stress has been calculated, because of the lack of techniques to assess wall shear stress in vivo. In recent years, techniques have been developed to accurately assess velocity profiles in arterioles, using fluorescently labeled particles as flow tracers, and non-invasively in large arteries by means of ultrasound or magnetic resonance imaging. Wall shear rate is derived from the in vivo recorded velocity profiles and wall shear stress is estimated as the product of wall shear rate and plasma viscosity in arterioles and whole blood viscosity in large arteries. In this review, we will discuss wall shear stress in vivo, paying attention to its assessment and especially to the results obtained in both arterioles and large arteries. The limitations of the methods currently in use are discussed as well. The data obtained in the arterial system in vivo are compared with the theoretically predicted ones, and the consequences of values deviating from theory for in vitro studies are considered. Applications of wall shear stress as in flow-mediated arterial dilation, clinically in use to assess endothelial cell (dys)function, are also addressed. This review starts with some background considerations and some theoretical aspects.

Productivity and R&D at the Firm Level in French Manufacturing

Abstract: In a companion study to that of Griliches and Mairesse for the United States, we have investigated the relationship between output, labor, and physical and RD it is less so in the time dimension. However, the within-firm estimates are still significant and of a likely order of magnitude.In this respect, they are more satisfactory than the U.S. ones. We show that this is largely due to a better measurement of the variables: (1) the fact that we can use a value-added measure of output instead of sales (or equivalently that we include materials among the factors of the production function); (2) the fact that we can correct the measures of labor, physical capital and output for the double counting or expensing out of the labor, capital and materials components of R&D expenditures.