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Virtual patients (VPs) are computer-based simulations of clinical scenarios used in health professions education to address various learning outcomes, including clinical reasoning (CR). CR is a crucial skill for health care practitioners, and its inadequacy can compromise patient safety. Recent advancements in large language models (LLMs) and social robots have introduced new possibilities for enhancing VP interactivity and realism. However, their application in VP simulations has been limited, and no studies have investigated the effectiveness of combining LLMs with social robots for CR training. The aim of the study is to explore the potential added value of a social robotic VP platform combined with an LLM compared to a conventional computer-based VP modality for CR training of medical students. A Swedish explorative proof-of-concept study was conducted between May and July 2023, combining quantitative and qualitative methodology. In total, 15 medical students from Karolinska Institutet and an international exchange program completed a VP case in a social robotic platform and a computer-based semilinear platform. Students' self-perceived VP experience focusing on CR training was assessed using a previously developed index, and paired 2-tailed t test was used to compare mean scores (scales from 1 to 5) between the platforms. Moreover, in-depth interviews were conducted with 8 medical students. The social robotic platform was perceived as more authentic (mean 4.5, SD 0.7 vs mean 3.9, SD 0.5; odds ratio [OR] 2.9, 95% CI 0.0-1.0; P=.04) and provided a beneficial overall learning effect (mean 4.4, SD 0.6 versus mean 4.1, SD 0.6; OR 3.7, 95% CI 0.1-0.5; P=.01) compared with the computer-based platform. Qualitative analysis revealed 4 themes, wherein students experienced the social robot as superior to the computer-based platform in training CR, communication, and emotional skills. Limitations related to technical and user-related aspects were identified, and suggestions for improvements included enhanced facial expressions and VP cases simulating multiple personalities. A social robotic platform enhanced by an LLM may provide an authentic and engaging learning experience for medical students in the context of VP simulations for training CR. Beyond its limitations, several aspects of potential improvement were identified for the social robotic platform, lending promise for this technology as a means toward the attainment of learning outcomes within medical education curricula.

Virtual patient (VP) simulations can be used to practice clinical reasoning (CR) in controlled learning environments. Traditional computer-based VP platforms often lack the authenticity and interactivity required for effective CR training. Artificial intelligence (AI)-enhanced social robotic VPs can enhance realism and engagement; however, quantitative evidence comparing them with conventional VP platforms remains limited. We compared medical students' experience of an AI-enhanced social robotic versus a conventional computer-based VP platform regarding the extent to which the design characteristics of the respective platform facilitate CR skill training. This observational crossover cohort study involved 178 sixth-semester medical students at Karolinska Institutet, Stockholm, Sweden (response rate: 42.3%; 178 of 421 invited students; Spring 2024-Spring 2025), who experienced both a large language model-enhanced social robotic VP platform supporting dialogue (social artificial intelligence-enhanced robotic interface [SARI]) and a conventional computer-based VP platform (virtual interactive case [VIC]) during their clinical rotation within rheumatology. Platform order was determined by clinical rotation scheduling. VP design was evaluated using a validated questionnaire across 5 domains: authenticity, professional approach, coaching quality, learning effects, and overall judgment. Students' CR training preferences were assessed using categorical responses and a Visual Analogue Scale, where a lower score favored SARI and a score of 5 indicated equal preference between platforms. SARI outperformed VIC across all 5 VP design domains. Students rated SARI higher for authenticity (median 4.0, IQR 3.5-4.5 vs 3.0, IQR 2.5-3.5; P<.001), professional approach (median 4.5, IQR 4.0-4.8 vs 4.0, IQR 3.5-4.5; P<.001), coaching quality (median 4.3, IQR 4.0-4.7 vs 4.0, IQR 3.7-4.7; P<.001), learning effect (median 4.4, IQR 4.0-5.0 vs 4.0, IQR 3.5-4.5; P<.001), and overall judgment (median 5.0, 4.0-5.0 vs 4.0, IQR 4.0-5.0; P<.001). Students strongly preferred SARI for CR training (72% vs 14%; odds ratio [OR] 27.1, 95% CI 14.3-53.7; P<.001), with Visual Analogue Scale scores confirming this preference (median 3.0, IQR 2.0-5.0; P<.001). Preferences were consistent across most subgroups (sex, prior VP experience, and platform order); in 2 subgroups, the difference was not significant, that is, students with prior VP experience (62% vs 38%; OR 2.6; 95% CI 0.8-8.9; P=.11) and students first introduced to VIC (55% vs 45%; OR 1.5; 95% CI 0.7-2.9; P=.33). Our findings provide the first quantitative evidence that AI-enhanced social robotic VPs offer superior design characteristics than conventional computer-based platforms for CR training in medical education. These results support the use of AI-driven social robots for VP simulations to better prepare medical students for real clinical encounters, and warrant future research on objective CR skill outcomes and long-term transfer to clinical practice. Unlike previous qualitative studies examining each platform separately, this study provides the first quantitative comparison of design characteristics between AI-enhanced social robotic and conventional computer-based VPs.

Background Medical students, often high-achieving and motivated, face unique challenges as they transition to clinical practice. This professional identity formation process requires adapting to patient contact, clinical reasoning, and high-stakes assessments such as OSCEs. Alongside these demands, students struggle with perfectionist expectations, heavy workloads, and patient responsibilities, leading to fears of failure, inadequacy, or professional unpreparedness. International research shows recurring concerns, including breaking bad news, managing patients, and feelings of exclusion among international students. However, existing studies are limited by their cross-sectional focus and site-specific scope, lacking longitudinal data or cross-country comparisons. Addressing this gap is crucial for understanding how concerns evolve over time and for shaping unified, high-quality medical education. This study aims to explore medical students’ fears across different countries. Methods In this study, we assessed the answers of medical students to the question, “What are you not looking forward to in your future job as a doctor?” This question was part of four questions of a longitudinal international survey aiming at students at the beginning and again towards the end of their studies. We analyzed responses given by medical students at the start of their studies at Ludwig-Maximilians University in Germany (LMU), Utrecht University in the Netherlands (UU), Karolinska Institutet in Sweden (KI) and the University of Nicosia in Cyprus (UNIC). A combination of qualitative analysis and hierarchical cluster analysis was used to identify recurring concerns, assess their importance, and examine differences based on university, gender, and societal context. Results A total of 2048 responses were collected between 2017 and 2021. The analysis identified two predominant categories: “stress” and “working conditions”. The category “failure” ranked third overall, though it did not appear among the top four response categories at LMU. Ranks 3 and 4 consisted of the categories “healthcare system”, “hierarchy”, “bad news” and “work-life balance”. The distribution of these categories varied between countries. Conclusions Quantifying students’ concerns and their prevalence enables international comparisons and highlights critical factors to address through systemic reforms targeted interventions. These findings provide valuable insights for improving students’ preparedness for medical practice.

IntroductionThe COVID-19 pandemic flooded intensive care units with patients needing supportive care. In Scandinavia, the greater Stockholm area was among the most affected. This study aimed to capture healthcare workers’ conditions and challenges during this prolonged crisis, including perspectives from the intensive care team.MethodsThe data consist of 22 semistructured individual interviews with regular and temporary healthcare workers involved in the intensive care of COVID-19 patients, including nurse assistants, registered nurses, critical care nurses and consultant and junior physicians. Thematic analysis was used to analyse the data.ResultsThe overarching theme that emerged was trying to create order in chaos.The theme encompassed four categories: adaptation with consequences, learning and growing while sacrificing my health, supporting and balancing staff resources without having enough, and challenging ICU values and standards. Each category comprised multiple subcategories.ConclusionOur study demonstrates challenges and identifies workarounds, support strategies and personnel learning experienced by COVID-19 intensive care teams in delivering patient care, ensuring patient safety and managing staff resilience. The findings can be used to better prepare for future crises.

IntroductionThe COVID-19 pandemic has had a profound effect on many domains of healthcare. Even in high-income countries such as Sweden, the number of patients has vastly outnumbered the resources in affected areas, in particular during the first wave. Staff caring for patients with COVID-19 in intensive care units (ICUs) faced a very challenging situation that continued for months. This study aimed to describe burnout, safety climate and causes of stress among staff working in COVID-19 ICUs.MethodA survey was distributed to all staff working in ICUs treating patients with COVID-19 in five Swedish hospitals during 2020 and 2021. The numbers of respondents were 104 and 603, respectively. Prepandemic data including 172 respondents from 2018 served as baseline.ResultsStaff exhaustion increased during the pandemic, but disengagement decreased compared with prepandemic levels (p<0.001). Background factors such as profession and work experience had no significant impact, but women scored higher in exhaustion. Total workload and working during both the first and second waves correlated positively to exhaustion, as did being regular ICU staff compared with temporary staff. Teamwork and safety climate remained unchanged compared with prepandemic levels.Respondents reported ‘making a mistake’ as the most stressful of the predefined stressors. Qualitative analysis of open-ended questions identified ‘lack of knowledge and large responsibility’, ‘workload and work environment’, ‘uncertainty’, ‘ethical stress’ and ‘organization and teamwork’ as major causes of stress.ConclusionDespite large workloads, disengagement at work was low in our sample, even compared with prepandemic levels. High levels of exhaustion were reported by the ICU staff who carried the largest workload. Multiple significant causes of stress were identified, with fear of making a mistake the most significant stressor.

Virtual patients (VPs) are increasingly used in medical education to train clinical reasoning (CR) skills. However, optimal VP design for enhancing interactivity and authenticity remains unclear. Novel interactive modalities, such as large language model (LLM)-enhanced social robotic VPs might increase interactivity and authenticity in CR skill practice. To evaluate medical students’ perceptions of CR training using an LLM-enhanced social robotic VP platform compared with a conventional computer-based VP platform. A qualitative study involved 23 third-year medical students from Karolinska Institutet, who completed VP cases on an LLM-enhanced social robotic platform and a computer-based semi-linear platform. In-depth interviews assessed students’ self-perceived acquirement of CR skills using the two platforms. Thematic analysis was employed to identify themes and sub-themes. Three main themes were identified: authenticity, VP application, and strengths and limitations. Students found the social robotic platform more authentic and engaging. It enabled highly interactive communication and expressed emotions, collectively offering a realistic experience. It facilitated active learning, hypothesis generation, and adaptive thinking. Limitations included lack of physical examination options and, occasionally, mechanical dialogue. The LLM-enhanced social robotic VP platform offers a more authentic and interactive learning experience compared to the conventional computer-based platform. Despite some limitations, it shows promise in training CR skills, communication, and adaptive thinking. Social robotic VPs may prove useful and safe learning environments for exposing medical students to diverse, highly interactive patient simulations.Key message•An LLM-powered social robotic VP platform provides a more authentic and interactive learning experience compared to conventional computer-based VPs.•Medical students undertaking clinical placements within rheumatology experienced that an LLM-enhanced social robotic platform can provide added value in training CR skills, particularly through realistic communication.•Social robotic VPs may prove useful and safe learning environments for exposing medical students to diverse, highly interactive patient simulations.

Glucocorticoids are essential for life, and are involved in growth, reproduction, intermediary metabolism, immune and inflammatory reactions as well as central nervous system and cardiovascular functions. Glucocorticoids are also used as treatment of many diseases. Resistance to exogenous glucocorticoids is sometimes seen in patients treated with glucocorticoids. Resistance to endogenous glucocorticoid is seen in some patients causing a syndrome called primary generalized glucocorticoid resistance.Glucocorticoids exert their effect through the glucocorticoid receptor, which belongs to the nuclear hormone receptor superfamily. The receptor consists of three functional domains, the N-terminal, the DNA binding domain and the ligand binding domain.The overall aim of this thesis was to study the glucocorticoid receptor in patients with primary generalized resistance to glucocorticoids i.e. resistance to endogenous glucocorticoids.In 12 unrelated patients with primary generalized glucocorticoid resistance we identified two novel mutations in the glucocorticoid receptor gene in two different patients, R477H and G679S respectively, situated in the DNA binding domain and in the ligand binding domain of the receptor. The R477H mutation is the only mutation described in the DNA binding domain of the human glucocorticoid receptor.We characterized these two mutations in vitro in terms of ligand binding, DNA binding, transactivation and transrepression as well as studies of crosstalking with the inflammatory transcription factor NFκB. We could demonstrate that the phenotype of the two patients expressing these two mutations correlated to the in vitro findings.We further demonstrated that the R477H and G679S were true mutations and not present as polymorphisms among healthy individuals.Glucocorticoid sensitivity among healthy individuals was also compared between two groups characterized as low and high secretors of urinary free cortisol studied with respect to their responses to a low dose of exogenous glucocorticoid. We concluded that individuals with a low cortisol profile, though still in the normal range, seems to be more sensitive to exogenous cortisol than those with high profile. This could have impact on the response to treatment with exogenous glucocorticoids and the prediction of therapeutic effect and adverse side effects.

BACKGROUNDUnderstanding outcomes and immunologic characteristics of cellular therapy recipients with SARS-CoV-2 is critical to performing these potentially life-saving therapies in the COVID-19 era. In this study of recipients of allogeneic (Allo) and autologous (Auto) hematopoietic cell transplant and CD19-directed chimeric antigen receptor T cell (CAR T) therapy at Memorial Sloan Kettering Cancer Center, we aimed to identify clinical variables associated with COVID-19 severity and assess lymphocyte populations.METHODSWe retrospectively investigated patients diagnosed between March 15, 2020, and May 7, 2020. In a subset of patients, lymphocyte immunophenotyping, quantitative real-time PCR from nasopharyngeal swabs, and SARS-CoV-2 antibody status were available.RESULTSWe identified 77 patients with SARS-CoV-2 who were recipients of cellular therapy (Allo, 35; Auto, 37; CAR T, 5; median time from cellular therapy, 782 days; IQR, 354-1611 days). Overall survival at 30 days was 78%. Clinical variables significantly associated with the composite endpoint of nonrebreather or higher oxygen requirement and death (n events = 25 of 77) included number of comorbidities (HR 5.41, P = 0.004), infiltrates (HR 3.08, P = 0.032), and neutropenia (HR 1.15, P = 0.04). Worsening graft-versus-host disease was not identified among Allo recipients. Immune profiling revealed reductions and rapid recovery in lymphocyte populations across lymphocyte subsets. Antibody responses were seen in a subset of patients.CONCLUSIONIn this series of Allo, Auto, and CAR T recipients, we report overall favorable clinical outcomes for patients with COVID-19 without active malignancy and provide preliminary insights into the lymphocyte populations that are key for the antiviral response and immune reconstitution.FUNDINGNIH grant P01 CA23766 and NIH/National Cancer Institute grant P30 CA008748.

Different models have been used in science and practice to identify instream large-wood (LW) sources and to estimate LW supply to rivers. This contribution reviews the existing models proposed in the last 35 years and compares two of the most recent geographic information system (GIS)-based models by applying them to 40 catchments in Switzerland. Both models, which we call here the empirical GIS approach (EGA) and fuzzy-logic GIS approach (FGA), consider landslides, debris flows, bank erosion, and mobilization of instream wood as recruitment processes and compute volumetric estimates of LW supply based on three different scenarios of process frequency and magnitude. Despite being developed following similar concepts and fed with similar input data, the results from the two models differ markedly. In general, estimated supply wood volumes were larger in each of the scenarios when computed with the FGA and lower with the EGA models. Landslides were the dominant process identified by the EGA, whereas bank erosion was the predominant process according to the FGA model. These differences are discussed, and results are compared to available observations coming from a unique database. Regardless of the limitations of these models, they are useful tools for hazard assessment, the design of infrastructure, and other management strategies.

Background and purpose Cancer patients treated with targeted therapies (e.g., epidermal growth factor receptor inhibitors) are susceptible to dermatologic adverse events (AEs) including secondary skin infections. Whereas infections such as paronychia and cellulitis have been reported, nasal vestibulitis (NV) has not been described with the use of these agents. The aim of our study was to characterize NV in cancer patients treated with targeted therapies. Methods We utilized a retrospective chart review of cancer patients who had been referred to dermatology and were diagnosed with NV. We recorded data including demographics, referral reason, underlying malignancy, targeted anticancer regimen, NV treatment, and nasal bacterial culture results. Results One Hundred Fifteen patients were included in the analysis, of which 13 % experienced multiple NV episodes. Skin rash was the most common reason (90 %) for a dermatology referral. The most common underlying malignancies were lung (43 %), breast (19 %), and colorectal (10 %) cancer. Sixty-eight percent of patients had been treated with an EGFRI-based regimen. Nasal cultures were obtained in 60 % of episodes, of which 94 % were positive for one or more organisms. Staphylococcus aureus was the most commonly isolated organism [methicillin-sensitive S. aureus 43 %; methicillin-resistant S. aureus 3 %]. Conclusions We report the incidence and characteristics of an unreported, yet frequent dermatologic condition in cancer patients treated with targeted therapies. These findings provide the basis for additional studies to describe the incidence, treatment, and consequences of this event. A better understanding of NV would mitigate its impact on patients’ quality of life and risk for additional dermatologic AEs.