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

Nasal Cannula Apneic Oxygenation Prevents Desaturation During Endotracheal Intubation: An Integrative Literature Review

Patients requiring emergency airway management may be at greater risk of acute hypoxemic events because of underlying lung pathology, high metabolic demands, insufficient respiratory drive, obesity, or the inability to protect their airway against aspiration. Emergency tracheal intubation is often required before complete information needed to assess the risk of procedural hypoxia is acquired (i.e., arterial blood gas level, hemoglobin value, or chest radiograph). During pre-oxygenation, administering high-flow nasal oxygen in addition to a non-rebreather face mask can significantly boost the effective inspired oxygen. Similarly, with the apnea created by rapid sequence intubation (RSI) procedures, the same high-flow nasal cannula can help maintain or increase oxygen saturation during efforts to secure the tube (oral intubation). Thus, the use of nasal oxygen during pre-oxygenation and continued during apnea can prevent hypoxia before and during intubation, extending safe apnea time, and improve first-pass success attempts. We conducted a literature review of nasal-cannula apneic oxygenation during intubation, focusing on two components: oxygen saturation during intubation, and oxygen desaturation time. We performed an electronic literature search from 1980 to November 2017, using PubMed, Elsevier, ScienceDirect, and EBSCO. We identified 14 studies that pointed toward the benefits of using nasal cannula during emergency intubation

Nasal Cannula Apneic Oxygenation Prevents Desaturation During Endotracheal Intubation: An Integrative Literature Review

Patients requiring emergency airway management may be at greater risk of acute hypoxemic events because of underlying lung pathology, high metabolic demands, insufficient respiratory drive, obesity, or the inability to protect their airway against aspiration. Emergency tracheal intubation is often required before complete information needed to assess the risk of procedural hypoxia is acquired (i.e., arterial blood gas level, hemoglobin value, or chest radiograph). During pre-oxygenation, administering high-flow nasal oxygen in addition to a non-rebreather face mask can significantly boost the effective inspired oxygen. Similarly, with the apnea created by rapid sequence intubation (RSI) procedures, the same high-flow nasal cannula can help maintain or increase oxygen saturation during efforts to secure the tube (oral intubation). Thus, the use of nasal oxygen during pre-oxygenation and continued during apnea can prevent hypoxia before and during intubation, extending safe apnea time, and improve first-pass success attempts. We conducted a literature review of nasal-cannula apneic oxygenation during intubation, focusing on two components: oxygen saturation during intubation, and oxygen desaturation time. We performed an electronic literature search from 1980 to November 2017, using PubMed, Elsevier, ScienceDirect, and EBSCO. We identified 14 studies that pointed toward the benefits of using nasal cannula during emergency intubation

Where in the World is this Image? Transformer-based Geo-localization in the Wild

Predicting the geographic location (geo-localization) from a single ground-level RGB image taken anywhere in the world is a very challenging problem. The challenges include huge diversity of images due to different environmental scenarios, drastic variation in the appearance of the same location depending on the time of the day, weather, season, and more importantly, the prediction is made from a single image possibly having only a few geo-locating cues. For these reasons, most existing works are restricted to specific cities, imagery, or worldwide landmarks. In this work, we focus on developing an efficient solution to planet-scale single-image geo-localization. To this end, we propose TransLocator, a unified dual-branch transformer network that attends to tiny details over the entire image and produces robust feature representation under extreme appearance variations. TransLocator takes an RGB image and its semantic segmentation map as inputs, interacts between its two parallel branches after each transformer layer, and simultaneously performs geo-localization and scene recognition in a multi-task fashion. We evaluate TransLocator on four benchmark datasets - Im2GPS, Im2GPS3k, YFCC4k, YFCC26k and obtain 5.5%, 14.1%, 4.9%, 9.9% continent-level accuracy improvement over the state-of-the-art. TransLocator is also validated on real-world test images and found to be more effective than previous methods.Comment: Accepted in ECCV 202