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

Comparing the efficacy of disease-modifying therapies in multiple sclerosis

Establishing the relative efficacy and safety of the different disease modifying therapies (DMTs) in multiple sclerosis (MS) is critical to the choice of agent that clinicians recommend for individual MS patients. The best evidence for the relative efficacy of the different DMTs comes from head-to-head randomized clinical trials (RCTs). Understanding that outcome-measures with the best established validity are the relapse rate and the actual (not the “confirmed”) change in the extended disability status scale (EDSS), we conclude from these head-to-head RCTs that interferon-beta (IFNβ) given subcutaneously multiple times per week (either IFNβ-1b or IFNβ-1a) and glatiramer acetate (GA) are about equivalent in terms of efficacy and that both of these agents, as well as many of the other DMTs, are superior to weekly intramuscular IFNβ-1a. Nevertheless, as ever-newer agents with novel mechanisms of action are brought to the marketplace, such direct head-to-head trials are becoming increasingly impractical, raising the need for alternative methods to draw reasonable inferences from less rigorous clinical data. One possible approach to judging comparative efficacy is to make comparisons across clinical trials using the complimentary analytic methods of calculating both the relative risk/rate and the absolute risk/rate reductions. A consideration and application of this analytic approach is undertaken here. It is only with an understanding of the safety and efficacy of the different agents that we can select, together with the patient, the right agent for the right person. © 201