MDT data quality assessment at the Calibration Centres for the ATLAS experiment at LHC

ATLAS is a large multipurpose detector, presently in the final phase of construction at the CERN Large Hadron Collider accelerator. In ATLAS the muon detection is performed by a huge magnetic spectrometer, built with the Monitored Drift Tube technology. It consists of more than 1,000 chambers and 350,000 drift tubes, which have to be controlled to a spatial accuracy better than 10 μm and an efficiency close to 100%. Therefore, the automated monitoring of the detector is an essential aspect of the operation of the spectrometer. The quality procedure collects data from online and offline sources and from the Calibration Stream at the Calibration Centres, situated in Ann Arbor (Michigan), MPI (Munich) and INFN Rome. The assessment at the Calibration Centres is performed using the DQHistogramAnalyzer utility of the ATHENA package. This application checks the histograms in an automated way and, after a further inspection with a human interface, reports results and summaries. The analysis results are stored in an Oracle Database using the COOL LCG library, through a C++ object-oriented interface. In this study a complete description of the entire chain, from the calibration stream up to the database storage is presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85418/1/jpconf10_219_032062.pd

Online patient simulation training to improve clinical reasoning: a feasibility randomised controlled trial

Background Online patient simulations (OPS) are a novel method for teaching clinical reasoning skills to students and could contribute to reducing diagnostic errors. However, little is known about how best to implement and evaluate OPS in medical curricula. The aim of this study was to assess the feasibility, acceptability and potential effects of eCREST — the electronic Clinical Reasoning Educational Simulation Tool. Methods A feasibility randomised controlled trial was conducted with final year undergraduate students from three UK medical schools in academic year 2016/2017 (cohort one) and 2017/2018 (cohort two). Student volunteers were recruited in cohort one via email and on teaching days, and in cohort two eCREST was also integrated into a relevant module in the curriculum. The intervention group received three patient cases and the control group received teaching as usual; allocation ratio was 1:1. Researchers were blind to allocation. Clinical reasoning skills were measured using a survey after 1 week and a patient case after 1 month. Results Across schools, 264 students participated (18.2% of all eligible). Cohort two had greater uptake (183/833, 22%) than cohort one (81/621, 13%). After 1 week, 99/137 (72%) of the intervention and 86/127 (68%) of the control group remained in the study. eCREST improved students’ ability to gather essential information from patients over controls (OR = 1.4; 95% CI 1.1–1.7, n = 148). Of the intervention group, most (80/98, 82%) agreed eCREST helped them to learn clinical reasoning skills. Conclusions eCREST was highly acceptable and improved data gathering skills that could reduce diagnostic errors. Uptake was low but improved when integrated into course delivery. A summative trial is needed to estimate effectiveness