Gordon R. Bernard

TL;DR: In this paper, the authors used latent class analysis (LCA) to identify sepsis phenotypes using only clinical variables and combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, "host-pathogen model").

TL;DR: An RDE system, Pivot/Remote, is developed that eliminates the need for paper-based CRFs and creates an innovative, distributed database that minimizes the problem of managing dual databases and keeps CC personnel in the loop until all changes are made.

TL;DR: In this paper, the authors developed a sensitive, high-throughput, and efficient assay using liquid bead array technology for the detection and quantification of binding IgG against the receptor binding domain (RBD) of SARS-CoV-2.

TL;DR: This study designed deep neural networks with 3 different architectural configurations, and after training them with a corpus of 8,770 annotated tweets, used them to predict personal experience tweets from a set of 821 annotate tweets, demonstrating a significant amount of improvement in predicting personal health experience tweets byDeep neural networks over that by conventional classifiers.

TL;DR: There is no evidence of an impact on 30-day readmissions or mortality due to the postdischarge telephone call program in patients discharged home from a hospital general medicine service or usual care discharge.