Open Access

Simulation-based learning is being increasingly implemented across different domains of higher education to facilitate essential skills and competences (e.g. diagnostic skills, problem-solving, etc.). However, the lack of research that assesses and compares simulations used in different contexts (e.g., from design perspective) makes it challenging to effectively transfer good practices or establish guidelines for effective simulations across different domains. This study suggests some initial steps to address this issue by investigating the relations between learners' experience in simulation-based learning environments and learners' diagnostic accuracy across several different domains and types of simulations, with the goal of facilitating cross-domain research and generalizability. The findings demonstrate that used learners' experience ratings are correlated with objective performance measures, and can be used for meaningful comparisons across different domains. Measures of perceived extraneous cognitive load were found to be specific to the simulation and situation, while perceived involvement and authenticity were not. Further, the negative correlation between perceived extraneous cognitive load and perceived authenticity was more pronounced in interaction-based simulations. These results provide supporting evidence for theoretical models that highlight the connection between learners' experience in simulated learning environments and their performance. Overall, this research contributes to the understanding of the relationship between learners’ experience in simulation-based learning environments and their diagnostic accuracy, paving the way for the dissemination of best practices across different domains within higher education.

Background Lynch syndrome (LS) is not considered part of childhood cancer predisposition syndromes. Case presentation Analysis of a pediatric osteosarcoma (OS) displayed hypermutation (16.8), alternative lengthening of telomeres (ALT), loss of PMS2 expression in tumor tissue (retained in non-neoplastic cells), PMS2 loss of heterozygosity (LOH), and high-degree of microsatellite instability (MSI) tested by PCR. A heterozygous duplication c.1076dup p.(Leu359Phefs*6) in exon 10 of NM_000535.6:PMS2 was detected by SNV analysis in peripheral blood, confirming diagnosis of LS in the patient. The tumor molecular features suggest LS-associated development of OS. In a second case, whole-genome sequencing identified a heterozygous SNV c.1 A > T p.? in exon 1 of PMS2 in tumor and germline material of a girl with ependymoma. Tumor analysis displayed evidence for ALT and low mutational burden (0.6), PMS2 expression was retained, MSI was low. Multiplex ligation-dependent probe amplification identified no additional PMS2 variant and germline MSI testing did not reveal increased gMSI ratios in the patient´s lymphocytes. Thus, CMMRD was most closely excluded and our data do not suggest that ependymoma was related to LS in the child. Conclusions Our data suggest that the LS cancer spectrum may include childhood cancer. The importance of LS in pediatric cancers necessitates prospective data collection. Comprehensive molecular workup of tumor samples is necessary to explore the causal role of germline genetic variants.

Purpose Cancer predisposition syndromes are genetic disorders that significantly raise the risk of developing malignancies. Although the malignant manifestations of cancer predisposition syndromes are well-studied, recognizing their non-malignant features is crucial for early diagnosis, especially in children and adolescents. Methods A comprehensive literature search was conducted using the PubMed database, focusing on non-malignant manifestations of cancer predisposition syndromes in children and adolescents. Key sources included the Clinical Cancer Research pediatric oncology series and ORPHANET. Studies that described clinical signs and symptoms affecting specific organ systems were included. Results Non-malignant dermatological features often serve as early indicators of cancer predisposition syndromes, including café-au-lait spots in Neurofibromatosis Type 1 and facial angiofibromas in Tuberous Sclerosis Complex. Neurological and developmental anomalies such as cerebellar ataxia in ataxia-telangiectasia and intellectual disabilities in neurofibromatosis type 1 and tuberous sclerosis complex are significant indicators. Growth and metabolic anomalies are also notable, including overgrowth in Beckwith–Wiedemann syndrome and growth hormone deficiency in neurofibromatosis Type 1. In addition, facial anomalies, ocular manifestations, hearing issues, and thyroid anomalies are prevalent across various cancer predisposition syndromes. For instance, hearing loss may be significant in neurofibromatosis Type 2, while thyroid nodules are common in PTEN hamartoma tumor syndrome and DICER1 syndrome. Cardiovascular, abdominal, musculoskeletal, pulmonary, genitourinary manifestations, and prenatal deviations further complicate the clinical picture. Conclusions Recognizing non-malignant features of cancer predisposition syndromes is essential for early diagnosis and management. This organ-specific overview furthers awareness among healthcare providers, facilitating timely genetic counseling, surveillance programs, and preventive measures, ultimately improving patient outcomes.

As digitalization progresses and technologies advance rapidly, digital simulations offer great potential for learning professional practices in contexts such as medical or teacher higher education. The technological advancements increasingly facilitate the personalization of learning support to meet the individual needs of learners, whose diverse prerequisites influence their learning processes, activities, and outcomes. However, systematic approaches to combining technologies with educational theories and evidence are scarce. In this article, we propose to use data on relevant learning prerequisites and learning processes as a basis for personalizing feedback and scaffolding to facilitate learning with simulated practice representations. We connect theoretical concepts with methodological and technical approaches (e.g., using artificial intelligence) for modeling important learner variables as a basis for personalized learning support. The interplay between the learner and the simulation environment is outlined in a conceptual framework which may guide systematic research on personalized learning support in digital simulations.