Search

Your search keyword '"Hilgard, P."' showing total 929 results
Start Over Author "Hilgard, P."
929 results on '"Hilgard, P."'

1. Experience and impact of stigma in people with chronic hepatitis B: a qualitative study in Asia, Europe, and the United States

Author

Toumi, Mondher, Wallace, Jack, Cohen, Chari, Marshall, Chris, Kitchen, Helen, Macey, Jake, Pegram, Hannah, Slagle, Ashley F., Gish, Robert G., Ning, Qin, Yatsuhashi, Hiroshi, Cornberg, Markus, Brunetto, Maurizia, van Bömmel, Florian, Xie, Qing, Lee, Dee, Habuka, Noriyuki, Sbarigia, Urbano, Beumont-Mauviel, Maria, Keever, Angelina Villasis, Takahashi, Yasushi, Lu, Yiwei, Liu, Ao, Chen, Qiaoqiao, Ito, Tetsuro, Radunz, Olaf, Puggina, Anna, Hilgard, Gudrun, Chan, Eric K.H., and Wang, Su

Published
2024
Full Text
View/download PDF

2. Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation

Author

Wojcik, Stefan, Hilgard, Sophie, Judd, Nick, Mocanu, Delia, Ragain, Stephen, Hunzaker, M. B. Fallin, Coleman, Keith, and Baxter, Jay

Subjects
Computer Science - Social and Information Networks
Abstract

We present an approach for selecting objectively informative and subjectively helpful annotations to social media posts. We draw on data from on an online environment where contributors annotate misinformation and simultaneously rate the contributions of others. Our algorithm uses a matrix-factorization (MF) based approach to identify annotations that appeal broadly across heterogeneous user groups - sometimes referred to as "bridging-based ranking." We pair these data with a survey experiment in which individuals are randomly assigned to see annotations to posts. We find that annotations selected by the algorithm improve key indicators compared with overall average and crowd-generated baselines. Further, when deployed on Twitter, people who saw annotations selected through this bridging-based approach were significantly less likely to reshare social media posts than those who did not see the annotations.

Published
2022

3. Experience and impact of stigma in people with chronic hepatitis B: a qualitative study in Asia, Europe, and the United States

Author

Mondher Toumi, Jack Wallace, Chari Cohen, Chris Marshall, Helen Kitchen, Jake Macey, Hannah Pegram, Ashley F. Slagle, Robert G. Gish, Qin Ning, Hiroshi Yatsuhashi, Markus Cornberg, Maurizia Brunetto, Florian van Bömmel, Qing Xie, Dee Lee, Noriyuki Habuka, Urbano Sbarigia, Maria Beumont-Mauviel, Angelina Villasis Keever, Yasushi Takahashi, Yiwei Lu, Ao Liu, Qiaoqiao Chen, Tetsuro Ito, Olaf Radunz, Anna Puggina, Gudrun Hilgard, Eric K.H. Chan, and Su Wang

Subjects
Hepatitis B virus, Chronic Hepatitis B, Functional cure, Qualitative research, Self-stigma, Social impact, Public aspects of medicine, RA1-1270
Abstract

Abstract Background People with chronic hepatitis B (CHB) commonly experience social and self-stigma. This study sought to understand the impacts of CHB-related stigma and a functional cure on stigma. Methods Adults with CHB with a wide range of age and education were recruited from 5 countries and participated in 90-minute qualitative, semi-structured interviews to explore concepts related to CHB-associated stigma and its impact. Participants answered open-ended concept-elicitation questions regarding their experience of social and self-stigma, and the potential impact of reduced CHB-related stigma. Results Sixty-three participants aged 25 to 71 years (15 from the United States and 12 each from China, Germany, Italy, and Japan) reported emotional, lifestyle, and social impacts of living with CHB, including prejudice, marginalization, and negative relationship and work experiences. Self-stigma led to low self-esteem, concealment of CHB status, and social withdrawal. Most participants stated a functional cure for hepatitis B would reduce self-stigma. Conclusions CHB-related social and self-stigma are widely prevalent and affect many aspects of life. A functional cure for hepatitis B may reduce social and self-stigma and substantially improve the health-related quality of life of people with CHB. Incorporating stigma into guidelines along with infectivity considerations may broaden the patient groups who should receive treatment.

Published
2024
Full Text
View/download PDF

4. Rare HCV subtypes and retreatment outcomes in a cohort of European DAA-experienced patients

Author

Julia Dietz, Christiana Graf, Christoph P. Berg, Kerstin Port, Katja Deterding, Peter Buggisch, Kai-Henrik Peiffer, Johannes Vermehren, Georg Dultz, Andreas Geier, Florian P. Reiter, Tony Bruns, Jörn M. Schattenberg, Elena Durmashkina, Thierry Gustot, Christophe Moreno, Janina Trauth, Thomas Discher, Janett Fischer, Thomas Berg, Andreas E. Kremer, Beat Müllhaupt, Stefan Zeuzem, Christoph Sarrazin, C. Antoni, A. Teufel, R. Vogelmann, M. Ebert, J. Balavoine, E. Giostra, M. Berning, J. Hampe, T. Boettler, C. Neumann-Haefelin, R. Thimme, A. De Gottardi, A. Rauch, N. Semmo, V. Ellenrieder, M. Gress, A. Herrmann, A. Stallmach, D. Hoffmann, U. Protzer, A. Kodal, M. Löbermann, T. Götze, V. Keitel-Anselmino, C.M. Lange, R. Zachoval, J. Mayerle, A. Maieron, P. Michl, U. Merle, D. Moradpour, J.-P. Chave, M. Muche, H.-J. Epple, M. Müller-Schilling, F. Kocheise, T. Müller, F. Tacke, E. Roeb, J. Rissland, M. Krawczyk, P. Schulze, D. Semela, U. Spengler, J. Rockstroh, C.P. Strassburg, J. Siebler, J. Schulze zur Wiesch, F. Piecha, J. von Felden, S. Jordan, A. Lohse, M. Sprinzl, P. Galle, R. Stauber, B. Strey, W. Steckstor, W. Schmiegel, N.H. Brockmeyer, A. Canbay, C. Trautwein, F. Uschner, J. Trebicka, T. Weber, H. Wedemeyer, M. Cornberg, M. Manns, P. Wietzke-Braun, R. Günther, K. Willuweit, G. Hilgard, H. Schmidt, E. Zizer, J. Backhus, T. Seufferlein, O. Al-Taie, W. Angeli, S. Beckebaum, A. Erhardt, A. Garrido-Lüneburg, H. Gattringer, D. Genné, M. Gschwantler, F. Gundling, S. Hametner, R. Schöfl, S. Haag, H. Heinzow, T. Heyer, C. Hirschi, A. Jussios, S. Kanzler, N. Kordecki, M. Kraus, U. Kullig, S. Wollschläger, L. Magenta, B. Terziroli Beretta-Piccoli, M. Menges, L. Mohr, K. Muehlenberg, C. Niederau, B. Paulweber, A. Petrides, M. Pinkernell, R. Piso, W. Rambach, L. Reinhardt, M. Reiser, B. Riecken, A. Rieke, J. Roth, M. Schelling, P. Schlee, A. Schneider, D. Scholz, E. Schott, M. Schuchmann, U. Schulten-Baumer, A. Seelhoff, A. Stich, F. Stickel, J. Ungemach, E. Walter, A. Weber, H. Wege, T. Winzer, W. Abels, M. Adler, F. Audebert, C. Baermann, E. Bästlein, R. Barth, K. Barthel, W. Becker, J. Behrends, J. Benninger, F. Berger, D. Berzow, T. Beyer, M. Bierbaum, O. Blaukat, A. Bodtländer, G. Böhm, N. Börner, U. Bohr, B. Bokemeyer, H.R. Bruch, D. Bucholz, P. Buggisch, K. Matschenz, J. Petersen, O. Burkhard, N. Busch, C. Chirca, R. Delker, J. Diedrich, M. Frank, M. Diehl, A.O. Tal, M. Schneider, A. Dienethal, P. Dietel, N. Dikopoulos, M. Dreck, F. Dreher, L. Drude, K. Ende, U. Ehrle, K. Baumgartl, F. Emke, R. Glosemeyer, G. Felten, D. Hüppe, J. Fischer, U. Fischer, D. Frederking, B. Frick, G. Friese, B. Gantke, P. Geyer, H.R. Schwind, M. Glas, T. Glaunsinger, F. Goebel, U. Göbel, B. Görlitz, R. Graf, H. Gruber, C. Hartmann, C. Klag, G. Härter, M. Herder, T. Heuchel, S. Heuer, H. Hinrichsen, B. Seegers, K.-H. Höffl, H. Hörster, J.-U. Sonne, W.P. Hofmann, F. Holst, M. Hunstiger, A. Hurst, E. Jägel-Guedes, C. John, M. Jung, B. Kallinowski, B. Kapzan, W. Kerzel, P. Khaykin, M. Klarhof, U. Klüppelberg, Wolfratshausen, K. Klugewitz, B. Knapp, U. Knevels, T. Kochsiek, A. Körfer, A. Köster, M. Kuhn, A. Langekamp, B. Künzig, R. Link, M. Littman, H. Löhr, T. Lutz, P. Gute, G. Knecht, U. Lutz, D. Mainz, I. Mahle, P. Maurer, S. Mauss, C. Mayer, H. Möller, R. Heyne, D. Moritzen, M. Mroß, M. Mundlos, U. Naumann, O. Nehls, K, R. Ningel, A. Oelmann, H. Olejnik, K. Gadow, E. Pascher, A. Philipp, M. Pichler, F. Polzien, R. Raddant, M. Riedel, S. Rietzler, M. Rössle, W. Rufle, A. Rump, C. Schewe, C. Hoffmann, D. Schleehauf, W. Schmidt, G. Schmidt-Heinevetter, J. Schmidtler-von Fabris, L. Schneider, A. Schober, S. Niehaus-Hahn, J. Schwenzer, T. Seidel, G. Seitel, C. Sick, K. Simon, D. Stähler, F. Stenschke, H. Steffens, K. Stein, M. Steinmüller, T. Sternfeld, K. Svensson, W. Tacke, G. Teuber, K. Teubner, J. Thieringer, A. Tomesch, U. Trappe, J. Ullrich, G. Urban, S. Usadel, A. von Lucadou, F. Weinberger, M. Werheid-Dobers, P. Werner, T. Winter, E. Zehnter, and A. Zipf

Subjects
Direct-acting antivirals, Hepatitis C Virus, rare HCV genotypes, resistance-associated substitutions, treatment response, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Background and Aims: Data on the prevalence and characteristics of so-called rare HCV genotypes (GTs) in larger cohorts is limited. This study investigates the frequency of rare GT and resistance-associated substitutions and the efficacy of retreatment in a European cohort. Methods: A total of 129 patients with rare GT1-6 were included from the European resistance database. NS3, NS5A, and NS5B were sequenced and clinical parameters and retreatment efficacies were collected retrospectively. Results: Overall 1.5% (69/4,656) of direct-acting antiviral (DAA)-naive and 4.4% (60/1,376) of DAA-failure patients were infected with rare GT. Although rare GTs were almost equally distributed throughout GT1-6 in DAA-naive patients, we detected mainly rare GT4 (47%, 28/60 GT4; of these n = 17, subtype 4r) and GT3 (25%, 15/60 GT3, of these n = 8, subtype 3b) among DAA-failures. A total of 62% (37/60) of DAA failures had not responded to first-generation regimes and the majority was infected with rare GT4 (57%, 21/37). In contrast, among patients with failure to pangenotypic DAA regimens (38%, 23/60), infections with rare GT3 were overrepresented (57%, 13/23). Although NS5A RASs were uncommon in rare GT2, GT5a, and GT6, we observed combined RASs in rare GT1, GT3, and GT4 at positions 28, 30, 31, which can be considered as inherent. DAA failures with completed follow-up of retreatment, achieved a high SVR rate (94%, 45/48 modified intention-to-treat analysis; 92%, 45/49 intention-to-treat). Three patients with GT4f, 4r, or 3b, respectively, had virological treatment failure. Conclusions: In this European cohort, rare HCV GT were uncommon. Accumulation of specific rare GT in DAA-failure patients suggests reduced antiviral activities of DAA regimens. The limited global availability of pangenotypic regimens for first line therapy as well as multiple targeted regimens for retreatment could result in HCV elimination targets being delayed. Impact and implications: Data on the prevalence and characteristics of rare HCV genotypes (GT) in larger cohorts are still scarce. This study found low rates of rare HCV GTs among European HCV-infected patients. In direct-acting antiviral (DAA)-failure patients, rare GT3 subtypes accumulated after pangenotypic DAA treatment and rare GT4 after first generation DAA failure and viral resistance was detected at NS5A positions 28, 30, and 31. The limited global availability of pangenotypic DAA regimens for first line therapy as well as multiple targeted regimens for retreatment could result in HCV elimination targets being delayed.

Published
2024
Full Text
View/download PDF

5. Do explanations increase the effectiveness of AI-crowd generated fake news warnings?

Author

Epstein, Ziv, Foppiani, Nicolò, Hilgard, Sophie, Sharma, Sanjana, Glassman, Elena, and Rand, David

Subjects
Computer Science - Human-Computer Interaction
Abstract

Social media platforms are increasingly deploying complex interventions to help users detect false news. Labeling false news using techniques that combine crowd-sourcing with artificial intelligence (AI) offers a promising way to inform users about potentially low-quality information without censoring content, but also can be hard for users to understand. In this study, we examine how users respond in their sharing intentions to information they are provided about a hypothetical human-AI hybrid system. We ask i) if these warnings increase discernment in social media sharing intentions and ii) if explaining how the labeling system works can boost the effectiveness of the warnings. To do so, we conduct a study ($N=1473$ Americans) in which participants indicated their likelihood of sharing content. Participants were randomly assigned to a control, a treatment where false content was labeled, or a treatment where the warning labels came with an explanation of how they were generated. We find clear evidence that both treatments increase sharing discernment, and directional evidence that explanations increase the warnings' effectiveness. Interestingly, we do not find that the explanations increase self-reported trust in the warning labels, although we do find some evidence that participants found the warnings with the explanations to be more informative. Together, these results have important implications for designing and deploying transparent misinformation warning labels, and AI-mediated systems more broadly., Comment: Forthcoming in ICWSM 2022

Published
2021

7. Feature Attributions and Counterfactual Explanations Can Be Manipulated

Author

Slack, Dylan, Hilgard, Sophie, Singh, Sameer, and Lakkaraju, Himabindu

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

As machine learning models are increasingly used in critical decision-making settings (e.g., healthcare, finance), there has been a growing emphasis on developing methods to explain model predictions. Such \textit{explanations} are used to understand and establish trust in models and are vital components in machine learning pipelines. Though explanations are a critical piece in these systems, there is little understanding about how they are vulnerable to manipulation by adversaries. In this paper, we discuss how two broad classes of explanations are vulnerable to manipulation. We demonstrate how adversaries can design biased models that manipulate model agnostic feature attribution methods (e.g., LIME \& SHAP) and counterfactual explanations that hill-climb during the counterfactual search (e.g., Wachter's Algorithm \& DiCE) into \textit{concealing} the model's biases. These vulnerabilities allow an adversary to deploy a biased model, yet explanations will not reveal this bias, thereby deceiving stakeholders into trusting the model. We evaluate the manipulations on real world data sets, including COMPAS and Communities \& Crime, and find explanations can be manipulated in practice., Comment: arXiv admin note: text overlap with arXiv:2106.02666

Published
2021

8. Counterfactual Explanations Can Be Manipulated

Author

Slack, Dylan, Hilgard, Sophie, Lakkaraju, Himabindu, and Singh, Sameer

Subjects
Computer Science - Machine Learning
Abstract

Counterfactual explanations are emerging as an attractive option for providing recourse to individuals adversely impacted by algorithmic decisions. As they are deployed in critical applications (e.g. law enforcement, financial lending), it becomes important to ensure that we clearly understand the vulnerabilities of these methods and find ways to address them. However, there is little understanding of the vulnerabilities and shortcomings of counterfactual explanations. In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated. More specifically, we show counterfactual explanations may converge to drastically different counterfactuals under a small perturbation indicating they are not robust. Leveraging this insight, we introduce a novel objective to train seemingly fair models where counterfactual explanations find much lower cost recourse under a slight perturbation. We describe how these models can unfairly provide low-cost recourse for specific subgroups in the data while appearing fair to auditors. We perform experiments on loan and violent crime prediction data sets where certain subgroups achieve up to 20x lower cost recourse under the perturbation. These results raise concerns regarding the dependability of current counterfactual explanation techniques, which we hope will inspire investigations in robust counterfactual explanations.

Published
2021

9. Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

Author

Sühr, Tom, Hilgard, Sophie, and Lakkaraju, Himabindu

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction
Abstract

Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the proposal of fair ranking algorithms (e.g., Det-Greedy) which increase exposure of underrepresented candidates. However, there is little to no work that explores whether fair ranking algorithms actually improve real world outcomes (e.g., hiring decisions) for underrepresented groups. Furthermore, there is no clear understanding as to how other factors (e.g., job context, inherent biases of the employers) may impact the efficacy of fair ranking in practice. In this work, we analyze various sources of gender biases in online hiring platforms, including the job context and inherent biases of employers and establish how these factors interact with ranking algorithms to affect hiring decisions. To the best of our knowledge, this work makes the first attempt at studying the interplay between the aforementioned factors in the context of online hiring. We carry out a largescale user study simulating online hiring scenarios with data from TaskRabbit, a popular online freelancing site. Our results demonstrate that while fair ranking algorithms generally improve the selection rates of underrepresented minorities, their effectiveness relies heavily on the job contexts and candidate profiles.

Published
2020

10. Reliable Post hoc Explanations: Modeling Uncertainty in Explainability

Author

Slack, Dylan, Hilgard, Sophie, Singh, Sameer, and Lakkaraju, Himabindu

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

As black box explanations are increasingly being employed to establish model credibility in high-stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by state-of-the-art techniques are inconsistent, unstable, and provide very little insight into their correctness and reliability. In addition, these methods are also computationally inefficient, and require significant hyper-parameter tuning. In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty. We instantiate this framework to obtain Bayesian versions of LIME and KernelSHAP which output credible intervals for the feature importances, capturing the associated uncertainty. The resulting explanations not only enable us to make concrete inferences about their quality (e.g., there is a 95% chance that the feature importance lies within the given range), but are also highly consistent and stable. We carry out a detailed theoretical analysis that leverages the aforementioned uncertainty to estimate how many perturbations to sample, and how to sample for faster convergence. This work makes the first attempt at addressing several critical issues with popular explanation methods in one shot, thereby generating consistent, stable, and reliable explanations with guarantees in a computationally efficient manner. Experimental evaluation with multiple real world datasets and user studies demonstrate that the efficacy of the proposed framework.

Published
2020

11. From Predictions to Decisions: Using Lookahead Regularization

Author

Rosenfeld, Nir, Hilgard, Sophie, Ravindranath, Sai Srivatsa, and Parkes, David C.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks. But when deployed, learned models also affect how users act in order to improve outcomes, whether predicted or real. The standard approach to learning is agnostic to induced user actions and provides no guarantees as to the effect of actions. We provide a framework for learning predictors that are both accurate and promote good actions. For this, we introduce look-ahead regularization which, by anticipating user actions, encourages predictive models to also induce actions that improve outcomes. This regularization carefully tailors the uncertainty estimates governing confidence in this improvement to the distribution of model-induced actions. We report the results of experiments on real and synthetic data that show the effectiveness of this approach.

Published
2020

12. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

Author

Slack, Dylan, Hilgard, Sophie, Jia, Emily, Singh, Sameer, and Lakkaraju, Himabindu

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real-world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.

Published
2019

13. Learning Key-Value Store Design

Author

Idreos, Stratos, Dayan, Niv, Qin, Wilson, Akmanalp, Mali, Hilgard, Sophie, Ross, Andrew, Lennon, James, Jain, Varun, Gupta, Harshita, Li, David, and Zhu, Zichen

Subjects
Computer Science - Databases, Computer Science - Machine Learning
Abstract

We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as fundamentally different data structures to be seen as views of the very same overall design space, and 2) allow seeing new data structure designs with performance properties that are not feasible by existing designs. The core intuition behind the construction of design continuums is that all data structures arise from the very same set of fundamental design principles, i.e., a small set of data layout design concepts out of which we can synthesize any design that exists in the literature as well as new ones. We show how to construct, evaluate, and expand, design continuums and we also present the first continuum that unifies major data structure designs, i.e., B+tree, B-epsilon-tree, LSM-tree, and LSH-table. The practical benefit of a design continuum is that it creates a fast inference engine for the design of data structures. For example, we can predict near instantly how a specific design change in the underlying storage of a data system would affect performance, or reversely what would be the optimal data structure (from a given set of designs) given workload characteristics and a memory budget. In turn, these properties allow us to envision a new class of self-designing key-value stores with a substantially improved ability to adapt to workload and hardware changes by transitioning between drastically different data structure designs to assume a diverse set of performance properties at will.

Published
2019

14. Learning Representations by Humans, for Humans

Author

Hilgard, Sophie, Rosenfeld, Nir, Banaji, Mahzarin R., Cao, Jack, and Parkes, David C.

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Statistics - Machine Learning
Abstract

When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy. Here we propose a framework to directly support human decision-making, in which the role of machines is to reframe problems rather than to prescribe actions through prediction. Inspired by the success of representation learning in improving performance of machine predictors, our framework learns human-facing representations optimized for human performance. This "Mind Composed with Machine" framework incorporates a human decision-making model directly into the representation learning paradigm and is trained with a novel human-in-the-loop training procedure. We empirically demonstrate the successful application of the framework to various tasks and representational forms.

Published
2019

15. Posttraumatic stress disorder and diabetes-related outcomes in patients with type 1 diabetes

Author

Frederike Lunkenheimer, Alexander J. Eckert, Dörte Hilgard, Daniel Köth, Bernhard Kulzer, Ursula Lück, Blanca Lüdecke, Antonia Müller, Harald Baumeister, and Reinhard W. Holl

Subjects
Medicine, Science
Abstract

Abstract Mental comorbidities in patients with type 1 diabetes mellitus (T1D) are common, and can have a negative impact on acute blood glucose levels and long-term metabolic control. Information on the association of T1D and comorbid posttraumatic stress disorder (PTSD) with diabetes-related outcomes is limited. The aim was to examine the associations between a clinical diagnosis of PTSD and diabetes-related outcomes in patients with T1D. Patients with T1D and comorbid documented PTSD from the DPV database (n = 179) were compared to a group with T1D without PTSD (n = 895), and compared to a group with T1D without comorbid mental disorder (n = 895) by matching demographics (age, gender, duration of diabetes, therapy and migration background) 1:5. Clinical diabetes-related outcomes {body mass index (BMI), hemoglobin A1c (hbA1c), daily insulin dose, diabetic ketoacidosis (DKA), hypoglycemia, number of hospital admissions, number of hospital days} were analyzed, stratified by age groups (≤ 25 years vs. > 25 years). Patients with comorbid PTSD aged ≤ 25 years compared with patients without PTSD or patients without mental disorders had significantly higher HbA1c (8.71 vs. 8.30 or 8.24%), higher number of hospital admissions (0.94 vs. 0.44 or 0.32 per year) and higher rates of DKA (0.10 vs. 0.02 or 0.01 events/year). Patients with comorbid PTSD aged ≤ 25 years compared with patients without PTSD had significantly higher BMI (0.85 vs. 0.59) and longer hospital stays (15.89 vs.11.58 days) than patients without PTSD. Patients with PTSD > 25 years compared with patients without PTSD or without any mental comorbidities had significantly fewer hospital admissions (0.49 vs. 0.77 or 0.69), but a longer hospital length of stay (20.35 vs. 11.58 or 1.09 days). We found that PTSD in younger patients with T1D is significantly related to diabetes outcome. In adult patients with T1D, comorbid PTSD is associated with fewer, but longer hospitalizations. Awareness of PTSD in the care of patients with T1D should be raised and psychological intervention should be provided when necessary.

Published
2023
Full Text
View/download PDF

16. Mental disorders in children and adolescents with type 1 diabetes before and during the COVID-19 pandemic: results from the DPV registry.

Author

Müller-Godeffroy, Esther, Schmid, Stefanie, Reinauer, Christina, Galler, Angela, Hilgard, Dörte, Marshall, Louise, Kapellen, Thomas, Lilienthal, Eggert, Mönkemöller, Kirsten, Brosig, Burkhard, Prchla, Christine, and Holl, Reinhard W.

Abstract

The COVID-19 pandemic affected the mental health of children and adolescents in the general population, yet its impact on those with chronic conditions is relatively unknown. This study aimed to compare the incidences of comorbid mental disorders and substance misuse in children and adolescents with type 1 diabetes before and during the pandemic. A total of 42,975 patients aged 6–18 years from the multicentre DPV (Diabetes Prospective Follow-up) registry were included. Multivariable regression models were applied to compare newly diagnosed comorbid mental disorders, adjusted for demographic and clinical variables, among them the number of medical visits, during the pre-pandemic period (09/2017–02/2020) and the COVID-19 pandemic period (03/2020–08/2022). Analysing both sexes together, there were no differences in the incidence rates of overall mental disorders between the pandemic and the pre-pandemic period. However, girls showed an increased incidence rate (odds ratio 1.2, CI 1.1–1.3) during the pandemic. Adolescent girls also displayed higher incidence rates of depression, eating disorders, and self-harm. Substance misuse declined overall during the pandemic (odds ratio 0.8, CI 0.7–0.9). During the COVID-19 pandemic, we found higher incidence rates of overall mental disorders in girls, but not in boys and not in the total study population of children and adolescents with type 1 diabetes. Adolescent girls displayed increased incidence rates of depression, eating disorders, and self-harm. Substance misuse declined substantially. Clinicians should be aware of the high-risk group of adolescent girls during times of increased strain. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

17. Measuring Interest in Science: The Science Curiosity Scale

Author

Landrum, Asheley, Hilgard, Joseph, Akin, Heather, Li, Nan, and Kahan, Dan M.

Subjects
curiosity, science curiosity, scale, psychometrics, Item Response Theory
Abstract

In the current study, we present the methods for creating andvalidating a science curiosity scale. We find that the scalepresented here is unidimensional and highly reliable.Moreover, it predicts engagement with a science documentaryclip more accurately than do measures of science intelligenceor education. Although more steps are needed, this providesinitial evidence for the utility of our measure of sciencecuriosity.

Published
2016

22. GALAD Score Detects Early-Stage Hepatocellular Carcinoma in a European Cohort of Chronic Hepatitis B and C Patients

Author

Clemens Schotten, Bastian Ostertag, Jan-Peter Sowa, Paul Manka, Lars P. Bechmann, Gudrun Hilgard, Claudio Marquardt, Marc Wichert, Hidenori Toyoda, Christian M. Lange, Ali Canbay, Philip Johnson, Heiner Wedemeyer, and Jan Best

Subjects
hepatocellular carcinoma, HCC, chronic hepatitis B, chronic hepatitis C, HBV, HCV, Medicine, Pharmacy and materia medica, RS1-441
Abstract

Despite vaccination programs and direct antiviral treatments, the incidence of virus-related hepatocellular carcinoma (HCC) remains high, while ultrasound-based detection rates for early-stage HCC is continuously low. To address this insufficiency, we set out to characterize whether the GALAD score, which incorporates gender, age, and serum levels of AFP, AFP isoform L3 (AFP-L3), and des-gamma-carboxy-prothrombin (DCP), can improve early-stage HCC detection in a Caucasian HBV/HCV cohort. In a retrospective German single-center study, 182 patients with HBV, 223 with HCV and 168 with other etiology (OE) of chronic liver disease (CLD) were enrolled. HCC was confirmed in 52 HBV, 84 HCV and 60 OE CLD patients. The diagnostic performance of the single biomarkers in HCC detection was compared to the GALAD model. At initial diagnosis, most patients were at (very) early BCLC 0 (n = 14/7%) or A (n = 56/29%) or intermediate stage BCLC B (n = 93/47%) HCC in all three subgroups. In the BCLC 0/A cohort, GALAD exhibited an AUC of 0.94 discriminating HCC from non-HCC, surpassing AFP (AUC 0.86), AFP-L3 (AUC 0.83) and DCP (AUC 0.83). In the HBV population, GALAD achieved an AUC of 0.96, in HCV an AUC of 0.98 and in OE an AUC of 0.99, clearly superior to the biomarkers alone. Furthermore, in HCV patients GALAD showed a significantly higher specificity (89%) versus AFP (64%) alone. In chronic viral hepatitis, the GALAD model showed superior performance in detection of early-stage HCC, while exhibiting higher specificity in HCV patients compared to AFP alone. We conclude that the GALAD score shows potential for HCC surveillance in Caucasian HBV/HCV patients.

Published
2021
Full Text
View/download PDF

24. World Congress Integrative Medicine & Health 2017: Part one

Author

Benno Brinkhaus, Torkel Falkenberg, Aviad Haramati, Stefan N. Willich, Josephine P. Briggs, Merlin Willcox, Klaus Linde, Töres Theorell, Lisa M. Wong, Jeffrey Dusek, Darong Wu, David Eisenberg, Bettina Berger, Kathi Kemper, Beate Stock-Schröer, Hedda Sützl-Klein, Rosaria Ferreri, Gary Kaplan, Harald Matthes, Gabriele Rotter, Elad Schiff, Zahi Arnon, Eckhard Hahn, Christina M. Luberto, David Martin, Silke Schwarz, Diethard Tauschel, Andrew Flower, Harsha Gramminger, Hedwig H. Gupta, S. N. Gupta, Annette Kerckhoff, Christian S. Kessler, Andreas Michalsen, Eun S. Kim, Eun H. Jang, Rana Kim, Sae B. Jan, Martin Mittwede, Wiebke Mohme, Eran Ben-Arye, Massimo Bonucci, Bashar Saad, Thomas Breitkreuz, Elio Rossi, Rejin Kebudi, Michel Daher, Samaher Razaq, Nahla Gafer, Omar Nimri, Mohamed Hablas, Gunver Sophia Kienle, Noah Samuels, Michael Silbermann, Lena Bandelin, Anna-Lena Lang, Eva Wartner, Christoph Holtermann, Maxwell Binstock, Robert Riebau, Edin Mujkanovic, Holger Cramer, Romy Lauche, Andres Michalsen, Lesley Ward, Dominik Irnich, Wolfram Stör, Geoffrey Burnstock, Hans-Georg Schaible, Thomas Ots, Jost Langhorst, Tobias Sundberg, Catherina Amarell, Melanie Anheyer, Marion Eckert, Mercedes Ogal, Annette Schönauer, Birgit Reisenberger, Bernhard Brand, Dennis Anheyer, Gustav Dobos, Matthias Kroez, Aldo Ammendola, Jun J. Mao, Claudia Witt, Yufei Yang, Miriam Oritz, Markus Horneber, Petra Voiß, Alexandra von Rosenstiel, Catharina Amarell, Friedemann Schad, Marc Schläppi, Matthias Kröz, Arndt Büssing, Gil Bar-Sela, David Avshalomov, Samuel Attias, Sian Cotton, Miek Jong, Mats Jong, Christian Scheffer, Friedrich Edelhäuser, Abdullah AlBedah, Myeong Soo Lee, Mohamed Khalil, Keiko Ogawa, Yoshiharu Motoo, Junsuke Arimitsu, Masao Ogawa, Genki Shimizu, Rainer Stange, Karin Kraft, Kenny Kuchta, Kenji Watanabe, D Bonin, Harald Gruber, Sabine Koch, Urs Pohlmann, Christine Caldwell, Barbara Krantz, Ria Kortum, Lily Martin, Lisa S. Wieland, Ben Kligler, Susan Gould-Fogerite, Yuqing Zhang, John J. Riva, Michael Lumpkin, Emily Ratner, Liu Ping, Pei Jian, Gesa-Meyer Hamme, Xiaosong Mao, Han Chouping, Sven Schröder, Josef Hummelsberger, Michael Wullinger, Marc Brodzky, Christoff Zalpour, Julia Langley, Wendy Weber, Lanay M. Mudd, Peter Wayne, Clauda Witt, Wolfgang Weidenhammer, Vinjar Fønnebø, Heather Boon, Amie Steel, Andrea Bugarcic, Melisa Rangitakatu, Jon Adams, David Sibbritt, Jon Wardle, Matthew Leach, Janet Schloss, Helene Dieze, Nadine Ijaz, Michael Heinrich, George Lewith, Bertrand Graz, Daniela Adam, Linus Grabenhenrich, Miriam Ortiz, Sylvia Binting, Thomas Reinhold, Susanne Andermo, Johanna Hök Nordberg, Maria Arman, Manoj Bhasin, Xueyi Fan, Towia Libermann, Gregory Fricchione, John Denninger, Herbert Benson, David D. Martin, Inge Boers, Arine Vlieger, Michael Teut, Alexander Ullmann, Fabian Lotz, Stephanie Roll, Claudia Canella, Michael Mikolasek, Matthias Rostock, Jörg Beyer, Matthias Guckenberger, Josef Jenewein, Esther Linka, Claudia Six, Sarah Stoll, Roger Stupp, Claudia M. Witt, Elisabeth Chuang, Melissa D. McKee, Petra Klose, Silke Lange, Vincent C. H. Chung, Hoi L. C. Wong, Xin Y. Wu, Grace Y. G. Wen, Robin S. T. Ho, Jessica Y. L. Ching, Justin C. Y. Wu, Amanda Coakley, Jane Flanagan, Christine Annese, Joanne Empoliti, Zishan Gao, Xugang Liu, Shuguang Yu, Xianzhong Yan, Fanrong Liang, Christoph D. Hohmann, Nico Steckhan, Thomas Ostermann, Arion Paetow, Evelyn Hoff, Xiao-Yang Hu, Ruo-Han Wu, Martin Logue, Clara Blonde, Lily Y. Lai, Beth Stuart, Yu-Tong Fei, Michael Moore, Jian-Ping Liu, Michael Jeitler, Hannah Zillgen, Manuel Högl, Barbara Stöckigt, Georg Seifert, Christian Kessler, Talat Khadivzadeh, Maryam Hassanzadeh Bashtian, Shapour Badiee Aval, Habibollah Esmaily, Jihye Kim, Keun H. Kim, Carina Klocke, Stefanie Joos, Abdulrahman Koshak, Li Wie, Emad Koshak, Siraj Wali, Omer Alamoudi, Abdulrahman Demerdash, Majdy Qutub, Peter Pushparaj, Sigrid Kruse, Isabell Fischer, Nadine Tremel, Joseph Rosenecker, Brenda Leung, Wendy Takeda, Ning Liang, Xue Feng, Jian-ping Liu, Hui-juan Cao, Nina Shinday, Lisa Philpotts, Elyse Park, Gregory L. Fricchione, Gloria Yeh, Niki Munk, Arash Zakeresfahani, Trevor R. Foote, Rick Ralston, Karen Boulanger, Dominik Özbe, Elmar Gräßel, Katharina Luttenberger, Anna Pendergrass, Daniel Pach, Judit Bellmann-Strobl, Yinhui Chang, Laura Pasura, Bin Liu, Sven F. Jäger, Ronny Loerch, Li Jin, Katja Icke, Xuemin Shi, Friedemann Paul, Michaela Rütz, Andreas Lynen, Meike Schömitz, Maik Vahle, Nir Salomon, Alon Lang, Adi Lahat, Uri Kopylov, Shomron Ben-Horin, Ofir Har-Noi, Benjamin Avidan, Rami Elyakim, Dorit Gamus, Siew NG, Jessica Chang, Justin Wu, John Kaimiklotis, Dania Schumann, Ludovica Buttó, Dirk Haller, Caroline Smith, Sheryl de Lacey, Michael Chapman, Julie Ratcliffe, Neil Johnson, Jane Lyttleton, Clare Boothroyd, Paul Fahey, Bram Tjaden, Marja van Vliet, Herman van Wietmarschen, Wilfried Tröger, Pia Vuolanto, Paulina Aarva, Minna Sorsa, Kaija Helin, Claudia Wenzel, Iris Zoderer, Patricia Pammer, Patrick Simon, Gerhard Tucek, Kathrin Wode, Roger Henriksson, Lena Sharp, Anna Stoltenberg, Yang Xiao-ying, Li-qiong Wang, Jin-gen Li, Ying Wang, Lynda Balneaves, Rielle Capler, Chiara Bocci, Marta Guffi, Marina Paolini, Ilaria Meaglia, Patrizia Porcu, Giovanni B. Ivaldi, Simona Dragan, Petru Bucuras, Ana M. Pah, Marius Badalica-Petrescu, Florina Buleu, Gheorghe Hogea-Stoichescu, Ruxandra Christodorescu, Lan Kao, Yumin Cho, Nadja Klafke, Cornelia Mahler, Cornelia von Hagens, Lorenz Uhlmann, Martina Bentner, Andreas Schneeweiss, Andreas Mueller, Joachim Szecsenyi, Isabella Neri, Katharina Schnabel, Margit Cree, Ralf Suhr, Sonia Baccetti, Fabio Firenzuoli, Maria V. Monechi, Mariella Di Stefano, Gianni Amunni, Wendy Wong, Bingzhong Chen, Hakima Amri, Lucy Kotlyanskaya, Belinda Anderson, Roni Evans, Paul Marantz, Ryan Bradley, Cathryn Booth-LaForce, Heather Zwickey, Benjamin Kligler, Audrey Brooks, Mary J. Kreitzer, Patricia Lebensohn, Elisabeth Goldblatt, Neus Esmel-Esmel, Maria Jiménez-Herrera, Alexandra Jocham, Pascal O. Berberat, Antonius Schneider, Morgana Masetti, Henriette Murakozy, Marja Van Vliet, Rita Agdal, Fatemeh Atarzadeh, Amir M. Jaladat, Leila Hoseini, Fatemeh Amini, Chen Bai, Tiegang Liu, Zian Zheng, Yuxiang Wan, Jingnan Xu, Xuan Wang, He Yu, Xiaohong Gu, Babak Daneshfard, Majid Nimrouzi, Vahid Tafazoli, Seyed M. Emami Alorizi, Seyed A. Saghebi, Mohammad R. Fattahi, Alireza Salehi, Hossein Rezaeizadeh, Mohammad M. Zarshenas, Kealoha Fox, John Hughes, Nenad Kostanjsek, Stéphane Espinosa, Peter Fisher, Abdul Latif, Donald Lefeber, William Paske, Ali Ö. Öztürk, Gizemnur Öztürk, Wim Tissing, Marianne Naafs, Martine Busch, Mohammad R. Sanaye, Kilian Dräger, Brent Leininger, Kate Shafto, Jenny Breen, Ana P. Simões-Wüst, Carolina Moltó-Puigmartí, Martien van Dongen, Pieter Dagnelie, Carel Thijs, Shelley White, Solveig Wiesener, Anita Salamonsen, Trine Stub, Sergio Abanades, Mar Blanco, Laia Masllorens, Roser Sala, Shafekah Al-Ahnoumy, Dongwoon Han, Luzhu He, Ha Yun Kim, Da In Choi, Terje Alræk, Agnete Kristoffersen, Christel von Sceidt, Stig Bruset, Frauke Musial, Felix J. Saha, Heidemarie Haller, Hoda Azizi, Nayereh Khadem, Malihe Hassanzadeh, Nazanin Estiri, Hamideh Azizi, Fatemeh Tavassoli, Marzieh Lotfalizadeh, Reza Zabihi, Mahmoud Mohammadzadeh Shabestari, Reza Paeizi, Masoumeh Alvandi Azari, Hamidreza Bahrami-Taghanaki, Erik Baars, Anja De Bruin, Anne Ponstein, Sergio Segantini, Maria Valeria Monechi, Fabio Voller, Jürgen Barth, Alexandra Kern, Sebastian Lüthi, Anja Zieger, Fabius Otto, Ariel Beccia, Corina Dunlap, Brendan Courneene, Paula Bedregal, Alvaro Passi, Alfredo Rodríguez, Mayling Chang, Soledad Gutiérrez, Florian Beissner, Christine Preibisch, Annemarie Schweizer-Arau, Roxana Popovici, Karin Meissner, Sylvie Beljanski, Laura Belland, Laura Rivera-Reyes, Ula Hwang, Dominik Sethe, Dörte Hilgard, Peter Heusser, Felicity Bishop, Miznah Al-Abbadey, Katherine Bradbury, Dawn Carnes, Borislav Dimitrov, Carol Fawkes, Jo Foster, Hugh MacPherson, Lisa Roberts, Lucy Yardley, Michelle Holmes, Paul Little, Cyrus Cooper, Patrizia Bogani, Valentina Maggini, Eugenia Gallo, Elisangela Miceli, Sauro Biffi, Alessio Mengoni, Renato Fani, Nadine Brands-Guendling, Peter W. Guendling, Gert Bronfort, Mitch Haas, Craig Schulz, Xiangwei Bu, J. Wang, T. Fang, Z. Shen, Y. He, X. Zhang, Zhengju Zhang, Dali Wang, Fengxian Meng, Klaus Baumann, Eckhard Frick, Christoph Jacobs, Ralph-Achim Grünther, Désirée Lötzke, Sonny Jung, Daniela R. Recchia, Sibylle Robens, Josephin Stankewitz, Mika Jeitler, Chunhoo Cheon, Bo H. Jang, Seong G. Ko, Ching W. Huang, Yui Sasaki, Youme Ko, Anna Cheshire, Damien Ridge, David Peters, Maria Panagioti, Chantal Simon, Hyun J. Cho, Soo J. Choi, Young S. Jung, Hyea B Im, Kieran Cooley, Laura Tummon-Simmons, Rachel Wasson, Kristen Kraemer, Richard Sears, Carly Hueber, Gwendolyn Derk, JR Lill, Ruopeng An, Lois Steinberg, Lourdes Diaz Rodriguez, Francisca García-de la Fuente, Miguel De la Vega, Keyla Vargas-Román, Jonatan Fernández-Ruiz, Irene Cantarero-Villanueva, Francisca García-De la Fuente, Fanny Jiménez-Guerrero, Noelia Galiano-Castillo, Gualberto Diaz-Saez, José I. Torres-Jimenez, Olga Garcia-Gomez, Luis Hortal-Muñoz, Camino Diaz-Diez, Demijon Dicen, Helene Diezel, Jane Frawley, Alex Broom, Fei Dong, Xueyan Ma, Liyi Yan, Liqun Wu, Jiaju Ma, Jianhua Zhen, Julie Dubois, Pierre-Yves Rodondi, Sophia Schwartze, Barbara Trapp, and Dirk Cysarz

Subjects
Other systems of medicine, RZ201-999
Published
2017
Full Text
View/download PDF

25. Overestimation of Action-Game Training Effects: Publication Bias and Salami Slicing

Author

Joseph Hilgard, Giovanni Sala, Walter R. Boot, and Daniel J. Simons

Subjects
video game training, action game training, cognitive training, meta-analysis, publication bias, salami slicing, Psychology, BF1-990
Abstract

Does playing action video games improve performance on tests of cognitive ability? A recent meta-analysis (Bediou et al., 2018a) summarized the available evidence and concluded that it can. Their analysis, however, did not adequately correct for publication bias. We re-analyzed the same set of studies with more appropriate adjustments for publication bias and found minimal evidence for transfer of training to cognitive ability measures. Instead, it is possible that there are little or no benefits, just publication bias — the exclusion of non-significant results from the published literature. That bias may be the cause of a lab effect reported in the original meta-analysis. The meta-analysis showed that studies from the Bavelier lab (the senior author of the meta-analysis) reported larger effects than other labs. We show that many of these original studies distributed different outcomes from the same or highly overlapping sets of participants across publications without noting the overlap. This salami-slicing might contribute to the extent of publication bias in the literature. More compelling, independent, and transparent evidence is needed before concluding that action video game training transfers to performance on other cognitive tasks.

Published
2019
Full Text
View/download PDF

27. Horseshoe kidney

Author

Guglielmo Mantica and Hilgard Ackermann

Subjects
horseshoe kidney, ureteral obstruction, urinary tract infections, Medicine
Abstract

A 21-year-old male presented with a one day history of right flank pain and signs of severe sepsis. The recorded vital signs included a temperature of 39. Celsius, blood pressure of 95/65 mmHg and heart rate of 165. The laboratory investigations confirmed a raised C-reactive protein (CRP) of 485 mg/l but it was interesting to note thatthe white cell count and creatinine were respectively normal (5.85 x 109/l; 77 mol/l). Microbiological investigations confirmed Klebsiella pneumoniae (>106 cfu/mL) on urine culture whilst two blood cultures were negative. Intravenous fluid resuscitation was successful in restoring hemodynamic stability. Amoxicillin/clavulanic acid (1200mg) was administered and continued for seven days after confirmation on antibiotic sensitivity testing. After ultrasonography confirmed severe right hydronephrosis, a contrasted computed tomography (CT) scan confirmed a horseshoe kidney (A, B, C, D) with an obstructed right-sided renalmoiety secondary to right pelvi-ureteric junction (PUJ) obstruction (B) and urothelial enhancement suggestive of an infected collecting system. Horseshoe kidneys are the most common congenital renal abnormality with an incidence of 1:400-600. It is commonly asyptomatic and rarely require interventionunless treating complications such as ureteral obstruction (PUJ), urinary tract infections, urolithiasis andWilms Tumour. It is associated with Trisomy 18 and females with Turner syndrome. Our patient underwent a right percutaneous nephrostomy (PCN) and 500 millilitres of infected urine were aspirated. He made a complete recovery at discharge. An interval MAG3 renogram confirmed that both moeities had good and excretion. A pelvi-ureteric junction repair will be performed after 6 weeks.

Published
2018
Full Text
View/download PDF

28. Neuropathy in paediatric type 1 diabetes mellitus – clinical characterization and analysis of risk factors in the diabetes prospective follow-up registry DPV (Diabetes-Patienten-Verlaufsdokumentation)-registry.

Author

Pappa, Angeliki, Haeusler, Martin G., Tittel, Sascha R., Boettcher, Claudia, Hilgard, Doerte, Knauer-Fischer, Sabine, Pavel, Marianne, Woelfle, Joachim, and Holl, Reinhard W.

Abstract

Data on the prevalence, clinical features and risk factors associated with paediatric diabetic neuropathy (DN) are scarce. We retrospectively analysed data from the DPV registry, including patients under 20 years of age, treated for type 1 diabetes mellitus (T1D) between 2005 and 2021. Patients with non-diabetic neuropathy were excluded. Data came from centres in Austria, Germany, Luxembourg and Switzerland. 1,121 of the 84,390 patients included had been diagnosed with DN. Univariate analysis showed patients with DN to be older and predominantly female, with a longer duration of T1D, higher insulin dosages per kg and day, lower rates of insulin pump therapy, higher postprandial glucose-, higher HbA1c-and higher cholesterol levels, and higher diastolic and systolic blood pressure values. There was also a larger proportion of smokers and higher prevalence of diabetic retinopathy. Median duration of diabetes at diagnosis of DN was 8.3 years. Multivariable analysis, adjusted for demographics revealed an increased risk for DN among female patients and those who were older, underweight (BMI-SDS), smoked cigarettes or had a longer duration of T1D or higher levels of HbA1c and postprandial blood glucose. The presence of retinopathy and higher cholesterol levels were also linked to increased risk while not-using insulin pump therapy was not. DN can develop after just a short duration of T1D. Prevention may be achieved by a lowering of HbA1c-and postprandial glucose levels through improved glycaemic control. This warrants further investigation. The slight female predominance suggests further hormonal and genetic etiological factors. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

29. Pathological game use in adults with and without Autism Spectrum Disorder

Author

Christopher R. Engelhardt, Micah O. Mazurek, and Joseph Hilgard

Subjects
Autism spectrum disorder, Adults, Video games, Pathological game use, Video game addiction, Medicine, Biology (General), QH301-705.5
Abstract

This study tested whether adults with autism spectrum disorder (ASD) are at higher risk for pathological game use than typically developing (TD) adults. Participants included 119 adults with and without ASD. Participants completed measures assessing daily hours of video game use, percent of free time spent playing video games, and symptoms of pathological game use. The results indicated that adults with ASD endorsed more symptoms of video game pathology than did TD adults. This relationship was strong, enjoying 300,000-to-1 odds in Bayesian model comparison. Results also showed that adults with ASD spent more daily hours playing video games and spent a higher percent of their free time playing video games than did TD adults. Even after adjustment for these differences in daily video game hours and proportion of free time spent on games, model comparisons found evidence for a difference in game pathology scores associated with ASD status. Additionally, escapism motives for playing video games was associated with game pathology scores in both ASD and TD adults, replicating and extending a previous report. In conclusion, the risk for pathological game use appears larger in adults with ASD compared with TD adults. These findings point to pathological game use as a potentially important focus of clinical attention in adults with ASD.

Published
2017
Full Text
View/download PDF

30. Efficacy and safety of the siRNA JNJ-73763989 and the capsid assembly modulator JNJ-56136379 (bersacapavir) with nucleos(t)ide analogues for the treatment of chronic hepatitis B virus infection (REEF-1): a multicentre, double-blind, active-controlled, randomised, phase 2b trial

Author

Yuen, Man-Fung, Asselah, Tarik, Jacobson, Ira M, Brunetto, Maurizia Rossana, Janssen, Harry L A, Takehara, Tetsuo, Hou, Jin Lin, Kakuda, Thomas N, Lambrecht, Tom, Beumont, Maria, Kalmeijer, Ronald, Guinard-Azadian, Carine, Mayer, Cristiana, Jezorwski, John, Verbinnen, Thierry, Lenz, Oliver, Shukla, Umesh, Biermer, Michael, Bourgeois, Stefan, Vanwolleghem, Thomas, Nevens, Frederik, Horsmans, Yves, Van Vlierberghe, Hans, Nastri, Ana Catharina, Lacerda, Marcus, Ramji, Alnoor, Conway, Brian, Coffin, Carla, Janssen, Harry, Fung, Scott, Shafran, Stephen, Hou, Jin Lin, Sperl, Jan, Urbanek, Petr, Plisek, Stanislav, Hejda, Vaclav, Samuel, Didier, Lacombe, Karine, Zoulim, Fabien, Guyader, Dominique, Raffi, Francois, Asselah, Tarik, Bourliere, Marc, Hilleret, Marie-Noelle, Wedemeyer, Heiner, Schulze zur Wiesch, Julian, Sprinzl, Kathrin, van Boemmel, Florian, Hilgard, Gudrun, Sabranski, Michael, Arasteh, Keikawus, Chan, Henry LY, Yuen, Man Fung, Wong, Vincent Ws, Brunetto, Maurizia Rossana, Taliani, Gloria, Andreone, Pietro, Lampertico, Pietro, Kurosaki, Masayuki, Yatsuhashi, Hiroshi, Fujiwara, Kei, Takehara, Tetsuo, Kawaoka, Tomokazu, Asahina, Yasuhiro, Enomoto, Hirayuki, Yabushita, Kazuhisa, Notsumata, Kazuo, Takaguchi, Koichi, Kawabe, Naoto, Kato, Naoya, Koji, Ogawa, Namisaki, Tadashi, Suzuki, Yoshiyuki, Yoon, Jung-Hwan, Ahn, SangHoon, Lim, Young-Suk, Paik, Seung Woon, Kiew, Kuang Kiat, Mohamed, Rosmawati, Tan, Soek Siam, Lee, Yeong Yeh, Hlebowicz, Maria, Berak, Hanna, Gasiorowski, Jacek, Halota, Waldemar, Janczewska, Ewa, Geyvandova, Natalia, Morozov, Viacheslav, Andreeva, Alla, Gusev, Denis, Bessonova, Elena, Osipenko, Marina, Romanova, Svetlana, Gankina, Natalia, Sagalova, Olga, Stepanova, Tatiana, Crespo Garcia, Javier, Diago, Moises, Inmaculada, Fernandez, Calleja, Jose Luis, Forns, Xavier, Buti, Maria, Tangkijvanich, Pisit, Tanwandee, Tawesak, Piratvisuth, Teerha, Leerapun, Apinya, Yilmaz, Gurdal, Tabak, Ömer Fehmi, Akarca, Ulus Salih, Akova, Murat, Idilman, Ramazan, Forton, Daniel, Bell, David, Agarwal, Kosh, Kennedy, Patrick, Felizarta, Franco, Sulkowski, Mark, Nahass, Ronald, Rojter, Sergio, Jacobson, Ira, Korenblat, Kevin, and Gitlin, Norman

Abstract

JNJ-73763989 (JNJ-3989), a small interfering RNA, targets all hepatitis B virus (HBV) RNAs, reducing all HBV proteins. JNJ-56136379 (JNJ-6379; also known as bersacapavir), a capsid assembly modulator, inhibits HBV replication. We aimed to evaluate the efficacy (ie, antiviral activity) and safety of these therapeutics in combination with nucleos(t)ide analogues in patients with chronic hepatitis B.

Published
2023
Full Text
View/download PDF

31. Brief use of a specific gun in a violent game does not affect attitudes towards that gun

Author

Joseph Hilgard, Christopher R. Engelhardt, and Bruce D. Bartholow

Subjects
media violence, video games, product placement, firearms, public policy, bayesian analysis, Science
Abstract

Although much attention has been paid to the question of whether violent video games increase aggressive behaviour, little attention has been paid to how such games might encourage antecedents of gun violence. In this study, we examined how product placement, the attractive in-game presentation of certain real-world firearm brands, might encourage gun ownership, a necessary antecedent of gun violence. We sought to study how the virtual portrayal of a real-world firearm (the Bushmaster AR-15) could influence players' attitudes towards the AR-15 specifically and gun ownership in general. College undergraduates (N = 176) played one of four modified video games in a 2 (gun: AR-15 or science-fiction control) × 2 (gun power: strong or weak) between-subjects design. Despite collecting many outcomes and examining many potential covariates and moderators, experimental assignment did little to influence outcomes of product evaluations or purchasing intentions with regard to the AR-15. Attitudes towards public policy and estimation of gun safety were also not influenced by experimental condition, although these might have been better tested by comparison against a no-violence control condition. By contrast, gender and political party had dramatic associations with all outcomes. We conclude that, if product placement shapes attitudes towards firearms, such effects will need to be studied with stronger manipulations or more sensitive measures.

Published
2016
Full Text
View/download PDF

34. A Basic Reference Shelf on Learning Theory. A Series One Paper from ERIC at Stanford.

Author

Stanford Univ., CA. ERIC Clearinghouse on Educational Media and Technology. and Hilgard, Ernest R.

Abstract

This reference work briefly identifies the salient principles of learning theory under the subdivisions: 1) S-R theory, 2) cognitive theory, and 3) motivation and personality theory. It also describes various approaches to practical problems via unified theories of learning. A seven-item annotated bibliography on learning theory and its applications is included. (MS)

Published
1967

35. The Psychological Heuristics of Learning.

Author

Academy for Educational Development, Washington, DC. and Hilgard, Ernest R.

Abstract

Efforts to apply knowledge gained from the study of the psychology of learning to actual teaching have been largely marked by frustration--until recently--when programed learning and its special derivative, computer assisted instruction, made their appearance. In programed learning you know where the student is and what he is doing, and what he learned. Computer assisted instruction goes one further: it can make computations so that an analyzed record is available for each student at any time. There are apparently no limitations as to what a computer can be used to teach. Computer assisted instruction is soundly grounded in what we know about learning. It will not make the teacher dispensable, but it will alter his role so that the teacher is released to do that which only the exceptional teacher now does well. (Author/GO)

Published
1970

37. Bibliografia

Author

J. Arias Abellán, F. Fourneau, Joaquín Bosque Maurel, Montserrat Galera i Monegal, Francesc Nadal Piqué, A. López Gómez, Carolina Montoro Gurich, M.ª José Lozano de San Cleto, Hilgard O'reilly Sternberg, Alfredo S. C. Bolsi, P. Reques Velasco, V. Rodríguez Rodríguez, and Juan A. Cebrián

Subjects
Geography (General), G1-922
Published
2000

Catalog

Books, media, physical & digital resources
See catalog results