Fondation de France

To provide an ESSKA-ESA formal Consensus to help general practitioners in their daily clinical decisions regarding the diagnosis and treatment of traumatic anterior shoulder instability (TASI) according to age and timing after injury. Part 1 is dedicated to the history-taking, physical examination and imaging studies. Patients were categorised into three age groups: adolescents (<20 years), young adults (20-40 years) and older adults (>40 years). Two clinical scenarios were addressed: first-time and recurrent dislocations. A formal consensus process was followed, including a steering group (15 experts from 9 countries), a rating group (18 experts from 18 countries) and a reader group (28 experts from 13 countries). The steering group formulated 35 clinical questions, 12 of which related to Part 1. Each question was answered with a statement and grade of recommendation (A-D), based on literature and expert opinion. The rating group reviewed and refined the statements, followed by validation from the reader group for cultural adaptability. Consensus was reached with high agreement: median (range) 9 (8-9). One question received a grade A recommendation, nine were B, and two were C. Most debated topics included imaging to guide treatment decisions in first-time dislocations, assessment of glenoid bone loss, and soft tissue quality. Each age group presents unique diagnostic considerations: younger patients have higher recurrence risk and activity levels; older patients more often present with associated soft-tissue injuries. While physical exam findings were consistent across ages, associated injuries gain importance with age. The distinction between first-time and recurrent dislocations significantly impacts imaging needs. The ESSKA-ESA Consensus highlights that age and timing (first-time versus recurrent) influence the decision-making when managing TASI. All parameters related to history taking, physical exam and imaging studies should be considered according to the patient's age and the number of dislocation episodes. Level II.

This study highlights the complexity of addressing adolescent sexual health in pediatrics and underscores the need for in situ training and dedicated adolescent consultations. • Pediatricians often serve as the main point of care for adolescents, particularly those with chronic conditions requiring specialized follow-up. • Sexual health is a critical aspect of adolescent development, yet frequently under-addressed in pediatric care. • We provide qualitative insights into the lived experiences and perceptions of pediatric subspecialists regarding adolescent sexual health in a university hospital. • We identify multi-faceted barriers to effectively tackling sexual health with adolescents, and concrete local improvement strategies are proposed.

Graphical representations of data are pervasive in modern communication and are often used to convey socio-economic, scientific, and medical information. Despite their popularity, it is still unknown whether they can enhance the long-term retention of their content. We conducted a delayed-recall task with psychology undergraduates (N = 92), in which participants read about the evolution of a socio-economic phenomenon, with five to six datapoints presented as graphics, text, or table; recall was operationalized as correct reporting of the trend in the data, 2 h after the information was presented. We found that graphics facilitated the delayed recall of such trends. No advantage was found on immediate recall of trends or specific datapoints in another sample of participants (N = 80). Thus, even for equal initial encoding of data, and even for very concise materials, graphics facilitate long-term retention. Overall, the study reveals the potential of graphics as effective tools for enhancing memory retention and therefore highlights their valuable role in educational settings.

ABSTRACTBackgroundThis study aimed to establish a Severity Scale for influenza and other acute respiratory infections (ARI), requiring hospitalization, for surveillance and research purposes (the SevScale). Such a scale could aid the interpretation of data gathered from disparate settings. This could facilitate pooled analyses linking viral genetic sequencing data to clinical severity, bringing insights to inform influenza surveillance and the vaccine strain selection process.MethodsWe used a subset of data from the Global Influenza Hospital Surveillance Network database, including data from different geographical areas and income levels. To quantify the underlying concept of severity, an item response model was developed using 16 indicators of severity related to the hospital stay. Each patient in the dataset was assigned a Severity Score and a Severity Category (low, medium, or high severity). Finally, we compared the model scores across different subgroups.ResultsData from 9 countries were included, covering between 4 and 11 seasons from 2012 to 2022, with a total of 96,190 ARI hospitalizations. Not for all severity indicators data were available for all included seasons. Subgroups with a high percentage of patients in the high Severity Category included influenza A(H1N1)pdm09, age ≥ 50, lower‐middle income countries, and admission since the start of the COVID‐19 pandemic.ConclusionsThe initial model successfully highlighted severity disparities across patient subgroups. Repeating this exercise with new, more complete data would allow recalibration and validation of the current model. The SevScale proved to be a promising method to define severity for influenza vaccine strain selection, surveillance, and research.

Les nombreuses réformes éducatives et politiques de démocratisation visent à accueillir un public de plus en plus diversifié, promouvoir l’égalité et former une population plus qualifiée. Avec la hausse du nombre d’étudiant·es et des taux de réussite assez faibles, la réussite dans l’enseignement supérieur est un sujet au cœur des réflexions actuelles. Aujourd’hui, les définitions de la réussite sont repensées pour prendre en compte la diversité des parcours. L’objectif de ce travail est d’analyser les effets des variables socio-démographiques et de la scolarité antérieure des étudiant·es sur leurs parcours en licence, en réalisant un suivi de cohorte sur 4 305 étudiant·es inscrit·es à partir de 2016 en première année de licence à Aix-Marseille Université. Des régressions logistiques binomiales ont été menées pour étudier quatre types de parcours : le décrochage, la progression linéaire, le redoublement et la réorientation. Ces différents parcours ont été analysés en lien avec les profils socio-démographiques et scolaires des étudiant·es. Nos résultats montrent que les étudiant·es issu·es de milieux favorisés et ayant eu une scolarité antérieure réussie sont plus susceptibles de suivre des parcours traditionnels, linéaires et sans décrochage. Malgré la volonté de démocratisation, des inégalités demeurent présentes dans les parcours des étudiant·es.

Summary: Medical researchers obtain knowledge about the prevention and treatment of disability and disease using physical measurements and image data. To assist in this endeavor, feature extraction packages are available that are designed to collect data from the image structure. In this study, we aim to augment current works by adding to the current mix of shape-based features. The significance of shape-based features has been explored extensively in research for several decades, but there is no single package available in which all shape-related features can be extracted easily by the researcher. PyCellMech has been crafted to address this gap. The PyCellMech package extracts three classes of shape features, which are classified as one-dimensional, geometric, and polygonal. Future iterations will be expanded to include other feature classes, such as scale-space. Availability and implementation: PyCellMech is freely available at https://github.com/icm-dac/pycellmech.