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

Predictors of mortality in adults on treatment for human immunodeficiency virus-associated tuberculosis in Botswana : a retrospective cohort study

CITATION: Muyaya, L. M., Young, T. & Loveday, M. 2018. Predictors of mortality in adults on treatment for human immunodeficiency virus-associated tuberculosis in Botswana: a retrospective cohort study. Medicine, 97(16):e0486, doi:10.1097/MD.0000000000010486.The original publication is available at http://journals.lww.com/md-journalPublication of this article was funded by the Stellenbosch University Open Access Fund.Mortality in patients with human immunodeficiency virus (HIV)-associated tuberculosis (TB) is high, particularly in sub-Saharan Africa. This study aimed to compare mortality and predictors of mortality in those who were antiretroviral therapy (ART) naïve to those with prior ART exposure. This retrospective cohort study was conducted in Serowe/Palapye District, Botswana, a predominantly urban district with a large burden of HIV-associated TB with a high case fatality. Between January 1, 2013 and December 31, 2013, patients confirmed with HIV-associated TB were enrolled and followed up. Kaplan–Meier and Cox proportional hazard modeling was undertaken to identify predictors of mortality, with ART initiation included as time-updated variable. Among the 300 patients enrolled in the study, 131 had started ART before TB diagnosis (44%). There were 45 deaths. There was no difference in mortality between ART-naïve patients and those with prior ART exposure. In the multivariate analysis, no ART use during TB treatment (hazard ratio [HR]=5.6, 95% confidence interval [CI]=2.9–11; P<.001), opportunistic infections other than TB (HR=8.5, 95% CI=4–18.4; P=.013), age ≥60 years (HR=4.8, 95% CI=1.8–13; P=.002), hemoglobin <10g/dL (HR=2.4, 95% CI=1.3–4.5) and hepatotoxicity (HR=5, 95% CI=1.6–17; P=.007) were associated with increased mortality. In the subgroup analysis, among ART-naïve patients, no ART use during TB treatment (HR=8.1, 95% CI=3.4–19.4; P<.001), opportunistic infections other than TB (HR=16, 95% CI=6.2–42; P<.001), and hepatotoxicity (HR=8.3, 95% CI=2.6–27; P<.001) were associated with mortality. Among patients with prior ART exposure, opportunistic infections other than TB (HR=6, 95% CI=2.6–27; P<.001) were associated with mortality. Mortality in patients with HIV-associated TB is still high. To reduce mortality, close clinical monitoring of patients together with initiation of ART during TB treatment is indicated.https://journals.lww.com/md-journal/Fulltext/2018/04200/Predictors_of_mortality_in_adults_on_treatment_for.54.aspxPublisher's versio

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