Welspun USA

Timely diagnosis of SARS-CoV-2 is important for infection control and treatment. Real-time reverse transcriptase PCR (rRT-PCR) tests are the reference standard for diagnosis but often require a centralized laboratory, making them time-intensive and unsuitable for resource-limited settings. The Abbott ID NOW™ COVID-19 2.0 assay is a rapid point-of-care (POC), isothermal molecular test for qualitative detection of SARS-CoV-2. We prospectively evaluated its clinical performance against three reference rRT-PCR tests: Hologic Panther Fusion, Roche Cobas, and CDC 2019-nCoV RT-PCR Diagnostic Panel. Investigators enrolled 3,530 subjects, with 3,146 evaluable. In symptomatic subjects (n = 914), the test showed a positive percent agreement (PPA) of 91.7% (95% confidence interval [CI]: 87.8, 94.4) and a negative percent agreement (NPA) of 98.4% (95% CI: 97.1, 99.1). The PPA improved with lower cycle threshold (Ct) values: 94.7% (95% CI: 91.2, 97.2) for Ct ≤36, 97.6% (95% CI: 94.5, 99.2) for Ct ≤33, and 99.4% (95% CI: 96.8, 100.0) for Ct ≤30. Discordant results were observed among the three reference rRT-PCR tests across evaluable subjects with suspected COVID-19 infection. For 1,630 cases of symptomatic and asymptomatic subjects suspected of COVID-19, where all three rRT-PCR methods were evaluable, CDC test results differed the most, with 144 discordant results with Roche and 119 with Panther rRT-PCR tests. Roche and Panther test results differed in 67 cases. In summary, the Abbott ID NOW™ COVID-19 2.0 assay can serve as a valuable diagnostic tool in acute symptomatic subjects in point-of-care settings. IMPORTANCE:The Abbott ID NOWTM COVID-19 2.0 assay is a suitable rapid test for diagnosing COVID-19 in acute symptomatic subjects and can be used in point-of-care settings and low-resource settings. With results reported in 12 minutes or less, Abbott ID NOWTM COVID-19 2.0 facilitates timely diagnosis, enabling linkage to appropriate antiviral medication.

In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms are employed in routine clinical settings. We review the spectrum of errors that may result from computer-aided nodule detection. In our clinical practice, we have seen errors in nodule detection, nodule localization, and nodule characterization. Each of these categories are demonstrated with illustrative cases. Through these illustrative cases, readers can be more familiar with nuances and pitfalls generated by computer-aided detection software. Although computer-aided nodule detection software is rapidly advancing, radiologists still need to thoroughly review images with mindfulness of some of the errors that can be generated by AI platforms for nodule detection.

The purpose of this study is to evaluate the capability of large language models (LLMs) to perform data quality assessment on background data with respect to a study scenario. LLMs generate coherent and contextually relevant text in response to prompts. Using a chat interface and prompting the model in a conversational style, OpenAI’s DaVinci model was prompted to perform a data quality assessment of background data against a study scenario using a revised Pedigree Matrix. The model performed DQA across the temporal, geographic, and technology coverage indicators. The outputs were evaluated for correctness in reasoning as well as the final scoring. Prompts underwent several iterations in some cases to improve the correctness. The model was able to provide correct reasoning and scores for 100

AimUsing the Knowledge, Skills and Attitudes (KSAs) framework, the aim of this study was to explore the specific knowledge, skills and attitudes of adaptable nurse educators to help inform the preparation of current and future educators for smooth transitions during periods of change.BackgroundExternal events, such as hurricanes, earthquakes, floods and wildfires can force programs to relocate and suspend classes for several days or weeks. These natural disasters have the potential to have a negative impact on the number of nursing students graduating on time as well as the quality of the clinical education experience and preparation for practice. Many lessons about educator adaptability can be learned from the COVID-19 restrictions. Identifying the KSAs of adaptable nurse educators during the rapidly changing educational landscape provided the opportunity for a foundational needs assessment to guide the preparation of educators for seamless transitions during times of change.DesignTo identify the KSA’s of adaptable nurse educators, an exploratory qualitative study using focus groups was conducted. The study used thematic analysis.MethodsThe research team developed, and pilot-tested focus group interview questions based on content areas identified in the literature. Targeted questions included queries specific to the KSAs necessary for adaptation and successful teaching using simulation. Educators from pre-licensure nursing programs in the United States participated in one of five 60-minute focus groups held virtually via a secure online meeting platform.ResultsAdaptable nurse educators have knowledge of resources, ongoing assessment, evaluation and teaching strategies and an understanding of the skillsets of their colleagues. Their skills include leadership, teamwork, redesigning learning and assessment. They demonstrate qualities such as resilience, empathy, acceptance, openness and positivity.ConclusionWith the current nursing workforce crisis, external events cannot be allowed to slow academic progression and graduation from nursing programs. In this exploratory qualitative study using focus groups, the KSAs held by adaptable nurse educators were explored. The findings of this study highlight the importance of collaboration and teamwork in academic institutes. The findings can be used as the foundation for nursing programs to prepare for future external events.