Assessing Variability in End-of-Life Intensity of Care After Out-of-Hospital Cardiac Arrest

Out of hospital cardiac arrest (OHCA) affects over 300,000 Americans per year.1 Many factors affect the outcomes and overall OHCA survival in a community; some of these include an individual’s characteristics such as age, co-morbid conditions, availability of an AED on scene, time to CPR, and the characteristics of the hospital they are treated at.1,2 Directly following resuscitation from cardiac arrest, the individual is at risk of developing numerous problems caused by sequelae of ischemic injury sustained during the arrest. The national average rate of survival to discharge is only 10%.2,3 Many of these factors are modifiable and provide an opportunity to improve outcomes. In our project, we focus on lifesustaining procedures administered by hospitals upon receiving and admitting individuals experiencing OHCA. We used previously validated measures as defined by Barnato et al as “life sustaining end of life (EOL) measures”:4 • Intubation and mechanical ventilation • Tracheostomy • Gastrostomy tube insertion • Hemodialysis • Enteral/parenteral nutrition • CPRhttps://jdc.jefferson.edu/cwicposters/1035/thumbnail.jp

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe