University of Lisbon

About: University of Lisbon is a education organization based out in Lisbon, Lisboa, Portugal. It is known for research contribution in the topics: Population & European union. The organization has 19122 authors who have published 48503 publications receiving 1102623 citations. The organization is also known as: Universidade de Lisboa & Lisbon University.

Interpreting the clinical importance of group differences in chronic pain clinical trials: IMMPACT recommendations

Abstract: An essential component of the interpretation of results of randomized clinical trials of treatments for chronic pain involves the determination of their clinical importance or meaningfulness. This involves two distinct processes—interpreting the clinical importance of individual patient improvements and the clinical importance of group differences—which are frequently misunderstood. In this article, we first describe the essential differences between the interpretation of the clinical importance of patient improvements and of group differences. We then discuss the factors to consider when evaluating the clinical importance of group differences, which include the results of responder analyses of the primary outcome measure, the treatment effect size compared to available therapies, analyses of secondary efficacy endpoints, the safety and tolerability of treatment, the rapidity of onset and durability of the treatment benefit, convenience, cost, limitations of existing treatments, and other factors. The clinical importance of individual patient improvements can be determined by assessing what patients themselves consider meaningful improvement using well-described methods. In contrast, the clinical meaningfulness of group differences must be determined by a multi-factorial evaluation of the benefits and risks of the treatment and of other available treatments for the condition in light of the primary goals of therapy. Such determinations must be conducted on a case-by-case basis, and are ideally informed by patients and their significant others, clinicians, researchers, statisticians, and representatives of society at large.

A Unified in Vitro Evaluation for Apatite-Forming Ability of Bioactive Glasses and Their Variants

Abstract: The aim of this study was to propose and validate a new unified method for testing dissolution rates of bioactive glasses and their variants, and the formation of calcium phosphate layer formation on their surface, which is an indicator of bioactivity At present, comparison in the literature is difficult as many groups use different testing protocols An ISO standard covers the use of simulated body fluid on standard shape materials but it does not take into account that bioactive glasses can have very different specific surface areas, as for glass powders Validation of the proposed modified test was through round robin testing and comparison to the ISO standard where appropriate The proposed test uses fixed mass per solution volume ratio and agitated solution The round robin study showed differences in hydroxyapatite nucleation on glasses of different composition and between glasses of the same composition but different particle size The results were reproducible between research facilities Researchers should use this method when testing new glasses, or their variants, to enable comparison between the literature in the future

Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning

Abstract: This paper presents a new semisupervised segmentation algorithm, suited to high-dimensional data, of which remotely sensed hyperspectral image data sets are an example. The algorithm implements two main steps: 1) semisupervised learning of the posterior class distributions followed by 2) segmentation, which infers an image of class labels from a posterior distribution built on the learned class distributions and on a Markov random field. The posterior class distributions are modeled using multinomial logistic regression, where the regressors are learned using both labeled and, through a graph-based technique, unlabeled samples. Such unlabeled samples are actively selected based on the entropy of the corresponding class label. The prior on the image of labels is a multilevel logistic model, which enforces segmentation results in which neighboring labels belong to the same class. The maximum a posteriori segmentation is computed by the α-expansion min-cut-based integer optimization algorithm. Our experimental results, conducted using synthetic and real hyperspectral image data sets collected by the Airborne Visible/Infrared Imaging Spectrometer system of the National Aeronautics and Space Administration Jet Propulsion Laboratory over the regions of Indian Pines, IN, and Salinas Valley, CA, reveal that the proposed approach can provide classification accuracies that are similar or higher than those achieved by other supervised methods for the considered scenes. Our results also indicate that the use of a spatial prior can greatly improve the final results with respect to a case in which only the learned class densities are considered, confirming the importance of jointly considering spatial and spectral information in hyperspectral image segmentation.