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

Potentials of Digitalization in Sports Medicine : A Narrative Review

Rigamonti L, Albrecht U-V, Lutter C, Tempel M, Wolfarth B, Back DA. Potentials of Digitalization in Sports Medicine : A Narrative Review. Current Sports Medicine Reports. 2020;19(4):157-163.Digital transformation is becoming increasingly common in modern life and sports medicine, like many other medical disciplines, it is strongly influenced and impacted by this rapidly changing field. This review aims to give a brief overview of the potential that digital technologies can have for health care providers and patients in the clinical practice of sports medicine. We will focus on mobile applications, wearables, smart devices, intelligent machines, telemedicine, artificial intelligence, big data, system interoperability, virtual reality, augmented reality, exergaming, or social networks. While some technologies are already used in current medical practice, others still have undiscovered potential. Due to the diversity and ever changing nature of this field, we will briefly review multiple areas in an attempt to give readers some general exposure to the landscape instead of a thorough, deep review of one topic. Further research will be necessary to show how digitalization applications could best be used for patient treatments.Beteiligte Körperschaft: AG Digitalisierung der Dt. Gesellschaft für Orthopädie und Unfallchirurgi

Decaying vs Annihilating Dark Matter in Light of a Tentative Gamma-Ray Line

Recently reported tentative evidence for a gamma-ray line in the Fermi-LAT data is of great potential interest for identifying the nature of dark matter. We compare the implications for decaying and annihilating dark matter taking the constraints from continuum gamma-rays, antiproton flux and morphology of the excess into account. We find that higgsino and wino dark matter are excluded, also for nonthermal production. Generically, the continuum gamma-ray flux severely constrains annihilating dark matter. Consistency of decaying dark matter with the spatial distribution of the Fermi-LAT excess would require an enhancement of the dark matter density near the Galactic center.Comment: 29 pages, 9 figures; v2: typo in appendix correcte

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 science. © The Author(s) 2019. Published by Oxford University Press