a retrospective cohort analysis

Background Allergy immunotherapy is an effective treatment for patients with allergic rhinitis whose symptoms are unresolved with pharmacotherapy. Allergy immunotherapy for grass pollen-induced allergic rhinitis is available in three modalities: subcutaneous immunotherapy and sublingual immunotherapy as a tablet or drop. This study aimed to understand trends in allergy immunotherapy prescribing and practice patterns for grass allergies in adult and paediatric patients in Germany. Methods A retrospective cohort study was conducted using IMS Disease Analyzer in Germany. Patients with an allergy immunotherapy prescription for grass pollen (Anatomical Therapeutic Chemical [ATC] classification code V01AA02) from September 2005 to December 2012 were included in the study. General Practitioners (GPs), dermatologists, Ear, Nose and Throat (ENT)-specialists, paediatricians and pneumologists were included as the allergy immunotherapy prescribing physicians in the study. Descriptive analyses were conducted on patient characteristics at index and prescribing physician specialty; a test for trend was conducted for timing of initiation of first allergy immunotherapy prescription in each annual prescribing season. Results Eighteen thousand eight hundred fifty eligible patients were identified during the study period. The majority of patients received subcutaneous immunotherapy; however, the proportion of patients receiving sublingual immunotherapy tablets increased from 8 % in 2006/2007 to 29 % in 2011/2012 (p < 0.001). Initiation of subcutaneous immunotherapy and Oralair® generally peaked during each prescribing year in two seasons (September- October and January) while GRAZAX® prescriptions peaked in autumn (September- October). ENT-specialists and dermatologists were the largest allergy immunotherapy prescribers in adults, while paediatricians and ENT-specialists were the largest prescribers of allergy immunotherapy in paediatric patients. Conclusions Subcutaneous immunotherapy remained the dominant allergy immunotherapy modality for grass pollen-induced allergic rhinitis in Germany for adult and paediatric patients; however, there was a marked increase in proportion of patients receiving sublingual immunotherapy tablets from 2006/2007 to 2011/2012, after their introduction to the market in 2006. ENT- specialists, dermatologists and paediatricians were responsible for the majority of prescribing. The predominance of particular modalities within certain physician specialties likely reflects different treatment goals or needs

Relationship between molecular pathogen detection and clinical disease in febrile children across Europe: a multicentre, prospective observational study

BackgroundThe PERFORM study aimed to understand causes of febrile childhood illness by comparing molecular pathogen detection with current clinical practice.MethodsFebrile children and controls were recruited on presentation to hospital in 9 European countries 2016-2020. Each child was assigned a standardized diagnostic category based on retrospective review of local clinical and microbiological data. Subsequently, centralised molecular tests (CMTs) for 19 respiratory and 27 blood pathogens were performed.FindingsOf 4611 febrile children, 643 (14%) were classified as definite bacterial infection (DB), 491 (11%) as definite viral infection (DV), and 3477 (75%) had uncertain aetiology. 1061 controls without infection were recruited. CMTs detected blood bacteria more frequently in DB than DV cases for N. meningitidis (OR: 3.37, 95% CI: 1.92-5.99), S. pneumoniae (OR: 3.89, 95% CI: 2.07-7.59), Group A streptococcus (OR 2.73, 95% CI 1.13-6.09) and E. coli (OR 2.7, 95% CI 1.02-6.71). Respiratory viruses were more common in febrile children than controls, but only influenza A (OR 0.24, 95% CI 0.11-0.46), influenza B (OR 0.12, 95% CI 0.02-0.37) and RSV (OR 0.16, 95% CI: 0.06-0.36) were less common in DB than DV cases. Of 16 blood viruses, enterovirus (OR 0.43, 95% CI 0.23-0.72) and EBV (OR 0.71, 95% CI 0.56-0.90) were detected less often in DB than DV cases. Combined local diagnostics and CMTs respectively detected blood viruses and respiratory viruses in 360 (56%) and 161 (25%) of DB cases, and virus detection ruled-out bacterial infection poorly, with predictive values of 0.64 and 0.68 respectively.InterpretationMost febrile children cannot be conclusively defined as having bacterial or viral infection when molecular tests supplement conventional approaches. Viruses are detected in most patients with bacterial infections, and the clinical value of individual pathogen detection in determining treatment is low. New approaches are needed to help determine which febrile children require antibiotics.FundingEU Horizon 2020 grant 668303

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting