![]() #Centurion medical professional(Centurion) is a professional liability insurance company dedicated to providing sound, stable insurance products and quality related services to healthcare providers in Arizona, California, Florida, Georgia, Michigan, Tennessee and Texas. with annual sales of more than 10 billion and more than 16,000 employees worldwide. Centurion Medical Liability Protective Risk Retention Group, Inc. Medline is the largest privately-held manufacturer and distributor of high quality medical supplies and solutions in the U.S. When it comes to success, our philosophy is we're in it together. Centurion kits and dressings are manufactured in five facilities in Michigan, North Carolina, Pennsylvania, Arizona and Mexico. We believe that a robust benefits portfolio is a reflection of an environment that is caring of its employees. The secret to our success has been in attracting and retaining top talent. The all-cash transaction is expected to close early December. Benefits designed with your quality of life in mind. Activity (NIC12), Measuring, medical and controlling devices manufacturing CAN (334512). Medline claims to be the world's largest privately-owned manufacturer and marketer of medical supplies, with over $10 billion in global sales annually.Īll locations, including the Arizona location in Chandler, are expected to remain fully-functioning as part of this deal. Kompass Business Directory - CENTURION MEDICAL CORPORATION. Centurion has worked to capture and maintain the lead in. The day before, Illinois-based Medline said it will buy Centurion Medical Products, a Michigan-based medical supply manufacturer with locations in Michigan, North Carolina, Pennsylvania, Arizona and Mexico. Centurion Medical develops, manufactures, and sells medical products for the healthcare industry. Centurion 665 Aluminum Alloy Handled Hardened Steel Head Monster Lopper 2 in. On Thursday, the online retailer said supplying prescriptions may help it further enter into the healthcare space. Showing 1 Result for Centurion Medical Products. #Centurion medical softwareThe announcement comes at the same time that Amazon, already in the medical supply business, said it is considering getting into prescriptions.Īmazon already sells surgical equipment and some supplies, as well as medical software services. Two of the largest medical supply companies are merging, positioning the combined company to become the market leader. ![]()
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![]() Parasnis certainly recognizes this as an ongoing process. We should see more announcements from Adobe in the coming months and years as they build more AI-powered algorithms into the platform and expose them to developers for use in their own software. Whether or not Sensei can be the technology that drives the Adobe cloud platform for the next 10 years, Parasnis and the company at large are very much committed to that vision. 8211 E ADOBE DRIVE SOFTWAREThese are features that are essential to the Adobe software experience, which the company is packaging as an API and delivering to developers to use in their own software. The difference here is that it involves Adobe’s core functions, so it may be intelligent auto cropping and smart tagging in Adobe Experience Manager or AI-fueled visual stock search in Creative Cloud. They don’t have to worry about building something like storage or security from scratch because they can grab those features from a platform that has built-in expertise and provides a way to easily incorporate it into applications. Every cloud company from Box to Salesforce has been exposing their services for years, letting developers take advantage of their expertise so they can concentrate on their core knowledge. It’s actually a classic software platform play, whether the service involves AI or not. Once they refined them for use in-house, the next step was to open up these algorithms to third-party developers to build their own applications using Adobe’s AI tools. The company began thinking about the new cloud platform with the larger artificial intelligence goal in mind, building AI-fueled algorithms to handle core platform functionality. “AI will be the single most transformational force in technology,” he said, adding that Sensei is by far the thing he is spending the most time on.” Parasnis sees this as the key part of the company’s cloud platform strategy. They saw a lot of companies looking at a more general AI for developers, but they had a different vision, one tightly focussed on Adobe’s core functionality. That’s why around 18 months ago, the company made another strategic decision to develop AI as a core part of the new platform. Making Sensei of it allīut Adobe also needed to get its ducks in a row around AI. The company certainly sees immersive technology like AR and VR, as well as voice as something they need to start thinking about as a future bet - and their base platform had to be adaptable enough to support that. That meant that they had to build in flexibility to allow for these kinds of changes over time, maybe even ones they can’t anticipate just yet. There’s a huge challenge because so much can change over time, especially right now when technology is shifting so rapidly. It’s a massive lift and we have to architect to last a decade,” he said. “When we first started thinking about the next generation platform, we had to think about what do we want to build for. When Parasnis arrived, the next step was to rearchitect the base platform in a way that was flexible enough to last for at least a decade - yes, a decade. It has been a highly successful one, but Adobe knew it would take more than simply shedding boxed software to survive long-term. The company’s transition from selling boxed software to a subscription-based cloud company began in 2013, long before Parasnis came on board. Finally, he’s in charge of all the digital infrastructure and the IT organization - just a bit on his plate. That includes managing a multi-thousand person engineering team. He also is in charge of operational execution for the core cloud platform and all the engineering building out the platform - including AI and Sensei. Parasnis told me that he has a broad set of responsibilities starting with the typical CTO role of setting the tone for the company’s technology strategy, but it doesn’t stop there by any means. In March at the Adobe Summit, I sat down with Adobe executive vice president and CTO Abhay Parasnis, and talked about a range of subjects with him including the company’s goal to build a cloud platform for the next decade - and how AI is a big part of that. 8211 E ADOBE DRIVE FULLThese organizations understand that AI is going to be a game-changer, even if they might not have a full understanding of how that’s going to work just yet. There isn’t a software company out there worth its salt that doesn’t have some kind of artificial intelligence initiative in progress right now. ![]() ![]() is the vector of item parameters for item i, rji0 = 0, for all i, andĪ normalizing constant. For items i = 1.,I, the polytomous Rasch model is given by probabilities Let X, be the response to item i with realization x. It estimates item parameters, plots item characteristic curves, estimates person locations, and produces graphical tests offit.Ĭonsider I items, where item i has mi 1 response categories represented by the numbers 0., mi. This paper describes a SAS macro %rasch_cml that fits an IRT model, the polytomous Rasch model. Evaluation of model fit is crucial and many fit statistics exist, but the issue of fit can also be addressed graphically. Fitting observed data to an IRT model enables us to test if these requirements are met. The requirements (i)-(iv) are referred to as unidimensional-ity, monotonicity, local independence, and absence of differential item functioning (DIF), respectively. (iv) P(Xt = Xi \ Y,0) = P(X, = x, \ d) for all items i and all variables Y. This requirement is related to the requirement of nonredun-dancy. Underlying latent variable accounts for, and it is usual to impose the requirement of local independence One would expect two similar items to be highly correlated and to have an even higher correlation than what the (ii) d ^ E(Xt | d) is increasing for all items i. The two first requirements can be written as follows. Let d denote the latent variable, and let X = (Xi)i=1I denote the vector of item responses. These requirements are standard in educational tests where (i) items should deal with only one subject (e.g., not being a mixture of math and language items), (ii) the probability of a correct answer should increase with ability, (iii) items should not ask the same thing twice, and (iv) the difficulty of an item should depend only on the ability of the student, for example, an item should not have features that makes it easier for boys than for girls at the same level of ability. (iv) Items should function in the same way in any subpopulation. (iii) Items should be sufficiently different to avoid redundance. (ii) Items should increase with the underlying latent variable. (i) Items should measure only one latent variable. Before the ordering of subjects can be done in a meaningful way, a number of requirements must be met. Traditional applications in education often use dichotomous (correct/incorrect) item scoring, but poly-tomous items are common in other applications.įormally, IRT models deal with the situation where several questions (called items) are used for ordering of a group of subjects with respect to a unidimensional latent variable. In addition to educational and psychological testing, IRT models have been also used in other areas of research, for example, in health status measurement and evaluation of Patient-Reported Outcomes (PROs) like physical functioning and psychological well-being wich are typical in applications of IRT models. Item response theory (IRT) models were developed to describe probabilistic relationships between correct responses on a set of test items and continuous latent traits. Graphical presentations are included: plots of item characteristic curves (ICCs), and a graphical goodness-of-fit-test is also produced. The macro estimates item parameters using conditional maximum likelihood (CML) estimation and person locations using maximum likelihood estimator (MLE) and Warm's weighted likelihood estimation (WLE). This paper describes an SAS macro %rasch_cml that fits polytomous Rasch models. #Fitplot r9 softwareTraditionally, specialized software has been needed for this, but conditional maximum likelihood estimation can be done using standard software for fitting generalized linear models. This enables consistent estimation of item parameters without reference to the distribution of the latent variable in the population. For a simple class of IRT models, the Rasch models, conditional inference is feasible. IRT models are widely used but often rely on distributional assumptions about the latent variable. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. VasconcelosĬopyright © 2013 Karl Bang Christensen. Hindawi Publishing Corporation ISRN Computational Mathematics Volume 2013, Article ID 617475, 8 pages Ĭonditional Maximum Likelihood Estimation in Polytomous Rasch Models Using SASĭepartment of Biostatistics, University of Copenhagen, Denmark Correspondence should be addressed to Karl Bang Christensen Received 29 November 2012 Accepted 29 January 2013 Academic Editors: L. ![]() |
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