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In this 52-week randomized trial, the α 4 β 7 integrin antibody vedolizumab was effective in treating Crohn's disease. The incidence of serious adverse events was higher with vedolizumab than with placebo. Crohn's disease is a chronic inflammatory bowel disease. 1 Current treatments include glucocorticoids, immunosuppressive agents (i.e., azathioprine, mercaptopurine, or methotrexate), and tumor necrosis factor (TNF) antagonists. 1 – 3 Many patients do not have a response to therapy, 4 and treatments are associated with important toxic effects. 5 , 6 Natalizumab, a monoclonal antibody that modulates gut and brain lymphocyte migration by antagonizing α 4 β 1 and α 4 β 7 integrin–mediated interactions, 7 is efficacious in the treatment of multiple sclerosis 8 , 9 and Crohn's disease. 10 – 12 Its use in patients with Crohn's disease has been limited by the development in some patients of progressive multifocal . . .

Introduction Over the past decade, the potential for drug-associated progressive multifocal leukoencephalopathy (PML) has become an increasingly important consideration in certain drug development programmes, particularly those of immunomodulatory biologics. Whether the risk of PML with an investigational agent is proven (e.g. extrapolated from relevant experience, such as a class effect) or merely theoretical, the serious consequences of acquiring PML require careful risk minimisation and assessment. No single standard for such risk minimisation exists. Vedolizumab is a recently developed monoclonal antibody to α4β7 integrin. Its clinical development necessitated a dedicated PML risk minimisation assessment as part of a global preapproval regulatory requirement. Objective The aim of this study was to describe the multiple risk minimisation elements that were incorporated in vedolizumab clinical trials in inflammatory bowel disease patients as part of the risk assessment and minimisation of PML programme for vedolizumab. Methods A case evaluation algorithm was developed for sequential screening and diagnostic evaluation of subjects who met criteria that indicated a clinical suspicion of PML. An Independent Adjudication Committee provided an independent, unbiased opinion regarding the likelihood of PML. Results Although no cases were detected, all suspected PML events were thoroughly reviewed and successfully adjudicated, making it unlikely that cases were missed. Conclusion We suggest that this programme could serve as a model for pragmatic screening for PML during the clinical development of new drugs.

The demand for computing education in post-secondary education is growing. However, teaching staff hiring is not keeping pace, leading to increasing class sizes. As computers are becoming ubiquitous, classes are following suit by increasing their use of technology. These two defining factors of scaled classes require us to reconsider teaching practices that originated in small classes with little technology. Rather than seeing scaled classes as a problem that needs management, we propose it is an opportunity that lets us collect and analyze large, high dimensional data sets and enables us to conduct experiments at scale. One way classes are increasing their use of technology is moving content delivery and assessment administration online. Massive Open Online Courses (MOOCs) have taken this to an extreme by delivering all material online, having no face-to-face interaction, and allowing the class to include thousands of students at once. To understand how this changes the information needs of the teacher, we surveyed MOOC teachers and compared our results to prior work that ran similar surveys among teachers of smaller online courses. While our results were similar, we did find that the MOOC teachers surveyed valued qualitative data – such as forum activity and student surveys – more than quantitative data such as grades. The potential reason for these results is that teachers found quantitative data insufficient to monitor class dynamics, such as problems with course material and student thought processes. They needed a source of data that required less upfront knowledge of what the teacher wanted to look for and how to find it. With such data, their understanding of the students and class situation could be more holistic. Since qualitative data such as forum activity and surveys have an inherent selection bias, we focused on required, constructed-response assessments in the course. This reduced selection bias had the advantages of needing less upfront knowledge and focused attention on measuring how well students are learning the material. Also, since MOOCs have a high proportion of auditors, we moved to studying a large local class to have a complete sample. We applied qualitative and quantitative methods to analyze wrong answers from constructed- response, code-tracing question sets delivered through an automated grading system. Using emergent coding, we defined tags to represent ways that a student might arrive at a wrong answer and applied them to our data set. Since what we identified as frequent wrong answers occurred at a much higher rate than infrequent wrong answers, we found that analyzing only these frequent wrong answers provides a representative overview of the data. In addition, a content expert is more likely to be able to tag a frequent wrong answer than a random wrong answer. Using the wrong answer to tag(s) association, we built a student error model and designed a hint intervention within the automated grading system. We deployed an in situ experiment in a large introductory computer science course to understand the effectiveness of parameters in the model and compared two different kinds of hints: reteaching and knowledge integration [28]. A reteaching hint re-explained the concept(s) associated with the tag. A knowledge integration hint focused on pushing the student in the right direction without re-explaining anything, such as reminding them of a concept or asking them to compare two aspects of the assessment. We found it was straightforward to implement and deploy our intervention experiment because of the existing class technology. In addition, for our model, we found co-occurrence provides useful information to propagate tags to wrong answers that we did not inspect. However, we were unable to find evidence that our hints improved student performance on post-test questions compared to no hints at all. Therefore, we performed a preliminary, exploratory analysis to understand potential reasons why our results are null and to inform future work. We believe scaled classes are a prime opportunity to study learning. This work is an example of how to take advantage of this chance by first collecting and analyzing data from a scaled class and then deploying a scaled in situ intervention by using the scaled class’s technology. With this work, we encourage other researchers to take advantage of scaled classes and hope it can serve as a starting point for how to do so.

We describe a system called Qr-Hint that, given a (correct) target query Q* and a (wrong) working query Q, both expressed in SQL, provides actionable hints for the user to fix the working query so that it becomes semantically equivalent to the target. It is particularly useful in an educational setting, where novices can receive help from Qr-Hint without requiring extensive personal tutoring. Since there are many different ways to write a correct query, we do not want to base our hints completely on how Q* is written; instead, starting with the user's own working query, Qr-Hint purposefully guides the user through a sequence of steps that provably lead to a correct query, which will be equivalent to Q* but may still \"look\" quite different from it. Ideally, we would like Qr-Hint's hints to lead to the \"smallest\" possible corrections to Q. However, optimality is not always achievable in this case due to some foundational hurdles such as the undecidability of SQL query equivalence and the complexity of logic minimization. Nonetheless, by carefully decomposing and formulating the problems and developing principled solutions, we are able to provide provably correct and locally optimal hints through Qr-Hint. We show the effectiveness of Qr-Hint through quality and performance experiments as well as a user study in an educational setting.