Book Review - Communication Skills in Pharmacy Practice: A Practical Guide for Students and Practitioners

Communication Skills in Pharmacy Practice: A Practical Guide for Students and Practitioners (5th Edition) By Wiliam N Tindall, Robert S Beardsly and Carole L Kimberlin 2011, 242 pages, Lippincott Williams & Wilkin

Whose Side are Ethics Codes On? Power, Responsibility and the Social Good

The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the "social good", of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes.Comment: Conference on Fairness, Accountability, and Transparency (FAT* '20), January 27-30, 2020, Barcelona, Spain. Correcte

A MEC-based Extended Virtual Sensing for Automotive Services

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.This work was supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536). The work of Christian Vitale was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for Euro-pean Programmes, Coordination, and Development

A MEC-based Extended Virtual Sensing for Automotive Services

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.This work was supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536). The work of Christian Vitale was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for Euro-pean Programmes, Coordination, and Development

Predicting severe disease in patients diagnosed with seasonal influenza in the emergency department

OBJECTIVES: We sought to develop an evidence-based tool to risk stratify patients diagnosed with seasonal influenza in the emergency department (ED). METHODS: We performed a single-center retrospective cohort study of all adult patients diagnosed with influenza in a large tertiary care ED between 2008 and 2018. We evaluated demographics, triage vital signs, chest x-ray and laboratory results obtained in the ED. We used univariate and multivariate statistics to examine the composite primary outcome of death or need for intubation. We validated our findings in patients diagnosed between 2018 and 2020. RESULTS: We collected data from 3128 subjects; 2196 in the derivation cohort and 932 in the validation cohort. Medical comorbidities, multifocal opacities or pleural effusion on chest radiography, older age, elevated respiratory rate, hypoxia, elevated blood urea nitrogen, blood glucose, blood lactate, and red blood cell distribution width were factors associated with intubation or death. We developed the Predicting Intubation in seasonal Influenza Patients diagnosed in the ED (PIIPED) risk-stratification tool from these factors. The PIIPED tool predicted intubation or death with an area under the receiver operating characteristic curve (AUC) of 0.899 in the derivation cohort and 0.895 in the validation cohort. A version of the tool including only factors available at ED triage, before laboratory or radiographic evaluation, exhibited AUC of 0.852 in the derivation cohort and 0.823 in the validation cohort. CONCLUSION: Clinical findings during an ED visit predict severe outcomes in patients with seasonal influenza. The PIIPED risk stratification tool shows promise but requires prospective validation

Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy

Computer vision technology is being used by many but remains representative of only a few. People have reported misbehavior of computer vision models, including offensive prediction results and lower performance for underrepresented groups. Current computer vision models are typically developed using datasets consisting of manually annotated images or videos; the data and label distributions in these datasets are critical to the models' behavior. In this paper, we examine ImageNet, a large-scale ontology of images that has spurred the development of many modern computer vision methods. We consider three key factors within the "person" subtree of ImageNet that may lead to problematic behavior in downstream computer vision technology: (1) the stagnant concept vocabulary of WordNet, (2) the attempt at exhaustive illustration of all categories with images, and (3) the inequality of representation in the images within concepts. We seek to illuminate the root causes of these concerns and take the first steps to mitigate them constructively.Comment: Accepted to FAT* 202