22 research outputs found

    Does Discharge Pharmacy Affect Outcomes After Percutaneous Coronary Intervention?

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    Aims for Improvement To reduce the rate of this institution’s readmission rate in patients discharged after PCI, by identifying the current trends in readmission in relation to prescription and medication delivery practices over the past 1 year

    SGLT2 Inhibitors in Patients with Diabetes and Cardiovascular Disease

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    Problem Definition: Multiple studies (e.g. EMPA-REG, CANVAS) demonstrate that SGLT2 Inhibitors (Inh) improve cardiac outcomes in patients with Type II Diabetes (DM2) with comorbid Cardiovascular Disease (CVD) including Heart Failure and Coronary Artery Disease. SGLT2 Inhibitors are considered standard of care for patients with DM2 and CVD. Based on literature published in European Journal of Preventative Cardiology and JACC HF, our prediction is that physicians at Thomas Jefferson University Hospital Ambulatory Practices (TJUH) under-utilize SGLT2 Inh for patients with co-morbid CVD and DM2. Aims for Improvement: Within the Jefferson Healthcare System, we sought to determine: Future Interventions The percentage of patients with an indication for an SGLT2 Inhibitor who were actually being prescribed this. How often providers within the Jefferson system were prescribing these medications, and what the barriers to prescribing are. With this information, we hoped to increase the percentage of (qualifying) patients who are on these medications as part of standard of care by 20% within one year of intervention

    Supernova neutrino burst detection with the Deep Underground Neutrino Experiment

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    The Deep Underground Neutrino Experiment (DUNE), a 40-kton underground liquid argon time projection chamber experiment, will be sensitive to the electron-neutrino flavor component of the burst of neutrinos expected from the next Galactic core-collapse supernova. Such an observation will bring unique insight into the astrophysics of core collapse as well as into the properties of neutrinos. The general capabilities of DUNE for neutrino detection in the relevant few- to few-tens-of-MeV neutrino energy range will be described. As an example, DUNE's ability to constrain the Îœe spectral parameters of the neutrino burst will be considered

    Experiment Simulation Configurations Approximating DUNE TDR

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    The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment consisting of a high-power, broadband neutrino beam, a highly capable near detector located on site at Fermilab, in Batavia, Illinois, and a massive liquid argon time projection chamber (LArTPC) far detector located at the 4850L of Sanford Underground Research Facility in Lead, South Dakota. The long-baseline physics sensitivity calculations presented in the DUNE Physics TDR, and in a related physics paper, rely upon simulation of the neutrino beam line, simulation of neutrino interactions in the near and far detectors, fully automated event reconstruction and neutrino classification, and detailed implementation of systematic uncertainties. The purpose of this posting is to provide a simplified summary of the simulations that went into this analysis to the community, in order to facilitate phenomenological studies of long-baseline oscillation at DUNE. Simulated neutrino flux files and a GLoBES configuration describing the far detector reconstruction and selection performance are included as ancillary files to this posting. A simple analysis using these configurations in GLoBES produces sensitivity that is similar, but not identical, to the official DUNE sensitivity. DUNE welcomes those interested in performing phenomenological work as members of the collaboration, but also recognizes the benefit of making these configurations readily available to the wider community

    Neutrino interaction classification with a convolutional neural network in the DUNE far detector

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    The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects

    Supernova Neutrino burst detection with the Deep Underground Neutrino Experiment

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    Experiment simulation configurations approximating DUNE TDR

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    The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment consisting of a high-power, broadband neutrino beam, a highly capable near detector located on site at Fermilab, in Batavia, Illinois, and a massive liquid argon time projection chamber (LArTPC) far detector located at the 4850L of Sanford Underground Research Facility in Lead, South Dakota. The long-baseline physics sensitivity calculations presented in the DUNE Physics TDR, and in a related physics paper, rely upon simulation of the neutrino beam line, simulation of neutrino interactions in the near and far detectors, fully automated event reconstruction and neutrino classification, and detailed implementation of systematic uncertainties. The purpose of this posting is to provide a simplified summary of the simulations that went into this analysis to the community, in order to facilitate phenomenological studies of long-baseline oscillation at DUNE. Simulated neutrino flux files and a GLoBES configuration describing the far detector reconstruction and selection performance are included as ancillary files to this posting. A simple analysis using these configurations in GLoBES produces sensitivity that is similar, but not identical, to the official DUNE sensitivity. DUNE welcomes those interested in performing phenomenological work as members of the collaboration, but also recognizes the benefit of making these configurations readily available to the wider community

    Neutrino interaction classification with a convolutional neural network in the DUNE far detector

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    Prospects for beyond the standard model physics searches at the Deep Underground Neutrino Experiment

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