Optimized Duration of Clopidogrel Therapy Following Treatment with the Endeavor Zotarolimus-Eluting Stent in Real-World Clinical Practice (OPTIMIZE) Trial: Rationale and design of a large-scale, randomized, multicenter study

Background Current recommendations for antithrombotic therapy after drug-eluting stent (DES) implantation include prolonged dual antiplatelet therapy (DAPT) with aspirin and clopidogrel >= 12 months. However, the impact of such a regimen for all patients receiving any DES system remains unclear based on scientific evidence available to date. Also, several other shortcomings have been identified with prolonged DAPT, including bleeding complications, compliance, and cost. The second-generation Endeavor zotarolimus-eluting stent (E-ZES) has demonstrated efficacy and safety, despite short duration DAPT (3 months) in the majority of studies. Still, the safety and clinical impact of short-term DAPT with E-ZES in the real world is yet to be determined. Methods The OPTIMIZE trial is a large, prospective, multicenter, randomized (1: 1) non-inferiority clinical evaluation of short-term (3 months) vs long-term (12-months) DAPT in patients undergoing E-ZES implantation in daily clinical practice. Overall, 3,120 patients were enrolled at 33 clinical sites in Brazil. The primary composite endpoint is death (any cause), myocardial infarction, cerebral vascular accident, and major bleeding at 12-month clinical follow-up post-index procedure. Conclusions The OPTIMIZE clinical trial will determine the clinical implications of DAPT duration with the second generation E-ZES in real-world patients undergoing percutaneous coronary intervention. (Am Heart J 2012;164:810-816.e3.)

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Fidelity and game-based technology in management education

This study explores educational technology and management education by analyzing fidelity in game-based management education interventions. A sample of 31 MBA students was selected to help answer the research question: To what extent do MBA students tend to recognize specific game-based academic experiences, in terms of fidelity, as relevant to their managerial performance? Two distinct game-based interventions (BG1 and BG2) with key differences in fidelity levels were explored: BG1 presented higher physical and functional fidelity levels and lower psychological fidelity levels. Hypotheses were tested with data from the participants, collected shortly after their experiences, related to the overall perceived quality of game-based interventions. The findings reveal a higher overall perception of quality towards BG1: (a) better for testing strategies, (b) offering better business and market models, (c) based on a pace that better stimulates learning, and (d) presenting a fidelity level that better supports real world performance. This study fosters the conclusion that MBA students tend to recognize, to a large extent, that specific game-based academic experiences are relevant and meaningful to their managerial development, mostly with heightened fidelity levels of adopted artifacts. Agents must be ready and motivated to explore the new, to try and err, and to learn collaboratively in order to perform.