Dupilumab-induced eosinophilia in patients with diffuse type 2 chronic rhinosinusitis.

BACKGROUND Dupilumab, a monoclonal anti-IL-4Rα antibody, is approved for several type 2 mediated inflammatory diseases like asthma, atopic dermatitis, and diffuse type 2 chronic rhinosinusitis (CRS). Clinical studies had reported a transient increase in blood eosinophils during dupilumab therapy. This study aimed to assess the impact of elevated blood eosinophils on clinical outcome and to investigate the cause of high blood eosinophil levels under dupilumab therapy. METHODS Patients suffering from diffuse type 2 CRS treated with dupilumab were examined on days 0, 28, 90, and 180 after therapy start. Sino-Nasal-Outcome-Test Score (SNOT-22), Total Nasal Polyp Score (TNPS), and blood samples were collected. Cytokine measurements and proteomics analysis were conducted. Flow cytometry analysis measured receptor expression on eosinophils. RESULTS Sixty-eighty patients were included. Baseline eosinophilia ≥0.3G/L was observed in 63.2% of patients, and in 30.9% of patients, eosinophils increased by ≥0.5G/L under dupilumab. Subjects with eosinophilia ≥0.3G/L at baseline had the best SNOT-22 mean change compared to no eosinophilia. Eosinophil elevation during dupilumab therapy had no impact on clinical scores. The eosinophil adhesion molecule VCAM-1 decreased significantly during therapy in all patients. The chemokine receptor CXCR4 was significantly down- and IL-4 upregulated in subjects with eosinophil increase. CONCLUSION Our findings suggest that increased eosinophils in type 2 CRS are associated with a good clinical response to dupilumab. Patients with elevated IL-4 at baseline developed dupilumab-induced transient eosinophilia. We identified the downregulation of VCAM-1 and surface markers CD49d and CXCR4 on eosinophils as possible explanations of dupilumab-induced eosinophilia

Exploring the effects of human-centered AI explanations on trust and reliance

Transparency is widely regarded as crucial for the responsible real-world deployment of artificial intelligence (AI) and is considered an essential prerequisite to establishing trust in AI. There are several approaches to enabling transparency, with one promising attempt being human-centered explanations. However, there is little research into the effectiveness of human-centered explanations on end-users' trust. What complicates the comparison of existing empirical work is that trust is measured in different ways. Some researchers measure subjective trust using questionnaires, while others measure objective trust-related behavior such as reliance. To bridge these gaps, we investigated the effects of two promising human-centered post-hoc explanations, feature importance and counterfactuals, on trust and reliance. We compared these two explanations with a control condition in a decision-making experiment (N = 380). Results showed that human-centered explanations can significantly increase reliance but the type of decision-making (increasing a price vs. decreasing a price) had an even greater influence. This challenges the presumed importance of transparency over other factors in human decision-making involving AI, such as potential heuristics and biases. We conclude that trust does not necessarily equate to reliance and emphasize the importance of appropriate, validated, and agreed-upon metrics to design and evaluate human-centered AI

Topical NSAIDs for chronic musculoskeletal pain: systematic review and meta-analysis

A previous systematic review reported that topical NSAIDs were effective in relieving pain in chronic conditions like osteoarthritis and tendinitis. More trials, a better understanding of trial quality and bias, and a reclassification of certain drugs necessitate a new review. Studies were identified by searching electronic databases, and writing to manufacturers. We identified randomised, double blind trials comparing topical NSAID with either placebo or another active treatment, in adults with chronic pain. The primary outcome was a reduction in pain of approximately 50% at two weeks, and secondary outcomes were local and systemic adverse events and adverse event-related withdrawals. Relative benefit and number-needed-to-treat (NNT), and relative harm and number-needed-to-harm (NNH) were calculated, and the effects of trial quality, validity and size, outcome reported, and condition treated, were examined by sensitivity analyses. Twelve new trials were added to 13 trials from a previous review. Fourteen double blind placebo-controlled trials had information from almost 1,500 patients. Topical NSAID was significantly better than placebo with relative benefit 1.9 (95% confidence interval 1.7 to 2.2), NNT 4.6 (95% confidence interval 3.8 to 5.9). Results were not affected by trial quality, validity or size, outcome reported, or condition treated. Three trials with 764 patients comparing a topical with an oral NSAID found no difference in efficacy. Local adverse events (6%), systemic adverse events (3%), or the numbers withdrawing due to an adverse event were the same for topical NSAID and placebo. Topical NSAIDs were effective and safe in treating chronic musculoskeletal conditions for two weeks. Larger and longer trials are necessary to fully elucidate the place of topical NSAIDs in clinical practice

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

LoL, Why Do You Even Play? Validating the Motives for Online Gaming Questionnaire in the Context of League of Legends

Motives are essential concepts in understanding a playerâEurotms experience in video games. We report and describe the analysis of a widely used questionnaire to measure playersâEurotm motives in video games, the Motives for Online Gaming Questionnaire (MOGQ). The present research aimed to investigate the psychometric quality of the MOGQ in the context of League of Legends (LoL). The MOGQ is a 27-item self-report scale designed to measure the motives for playing online games. To this end, 256 participants completed an online survey asking about their experiences and motives to play LoL. Results of confirmatory and exploratory factor analyses indicate weaknesses in the original 7-factor model. By removing five conspicuous items from the original 7-factor model, we propose an alternative 22-item version of the MOGQ. Additional confirmatory and exploratory factor analyses results indicate that the 22-item version of the MOGQ is more suitable in the context of LoL than the original 27-item questionnaire. However, further investigation into the quality of this alternative version compared to the original questionnaire is needed

Trust Issues with Trust Scales: Examining the Psychometric Quality of Trust Measures in the Context of AI

Trust is crucial for human interaction with artificial intelligence (AI) and is frequently measured through questionnaires or rating scales. One commonly used questionnaire in AI research is the Trust between People and Automation scale (TPA). However, its psychometric quality has yet to be examined in the context of AI. More recently, a Trust Scale for Explainable AI (TXAI) was recommended but not empirically evaluated. In this study, we assessed the psychometric qualities of both scales, using confirmatory and exploratory factor analyses to test the scales' validity and coefficients alpha and omega for reliability estimation. Our results suggested good psychometric quality for the TXAI after removing one item. Concerning the TPA, acceptable quality was only achieved when using a two-factor model (trust and distrust) and after removing two items. We provide recommendations for using the two scales and evidence to distinguish trust and distrust as separate psychological constructs

LoL, Why Do You Even Play? Validating the Motives for Online Gaming Questionnaire in the Context of League of Legends

Motives are essential concepts in understanding a player's experience in video games. We report and describe the analysis of a widely used questionnaire to measure players' motives in video games, the Motives for Online Gaming Questionnaire (MOGQ). The present research aimed to investigate the psychometric quality of the MOGQ in the context of League of Legends (LoL). The MOGQ is a 27-item self-report scale designed to measure the motives for playing online games. To this end, 256 participants completed an online survey asking about their experiences and motives to play LoL. Results of confirmatory and exploratory factor analyses indicate weaknesses in the original 7-factor model. By removing five conspicuous items from the original 7-factor model, we propose an alternative 22-item version of the MOGQ. Additional confirmatory and exploratory factor analyses results indicate that the 22-item version of the MOGQ is more suitable in the context of LoL than the original 27-item questionnaire. However, further investigation into the quality of this alternative version compared to the original questionnaire is needed

Measuring Players’ Experience of Need Satisfaction in Digital Games: An Analysis of the Factor Structure of the UPEQ

In this work, we explore the factorial structure of the Ubisoft Perceived Experience Questionnaire (UPEQ) and its correlation with game enjoyment. For this purpose, an online survey was conducted on the experience with the video game League of Legends. Three hundred and sixty-nine participants provided information about their in- and out-of-game demographics and rated their experience with the game using the UPEQ and the subscale Interest/Enjoyment from the Intrinsic Motivation Inventory. Using confirmatory and exploratory factor analysis, we found weaknesses in the 3-factor model of the UPEQ and propose a 6- or 7-factor structure as a basis for new research and improvement of the UPEQ

Independent Validation of the Video Game Dispositional Flow Scale With League of Legends Players

Flow is a highly influential concept across many research domains. Because of the close links between enjoyment and flow, it is also a central concept in game research. Accordingly, many measurement instruments in the form of questionnaires have been developed to measure flow in video games. However, limited independent validation work has been carried out to date. In this project, we evaluated the recently developed Video Game Dispositional Flow Scale (VGDFS) in the context of League of Legends. An online survey posted on the online community reddit.com yielded a sample of N = 140. Confirmatory factor analysis did not support the originally proposed structure. Subsequent exploratory factor analysis resulted in inconclusive findings. Convergent validity with other questionnaires for the measurement of interest, enjoyment, and flow could be demonstrated in our data, although with unusually high correlations between the VGDFS and another flow measure. For divergent validity, correlations with questionnaires for the measurement of boredom and trait anxiety in the hypothesized direction could be found. Findings suggest that the VGDFS needs further evaluation in different video game contexts while also hinting at measurement problems possibly related to conceptual issues with flow