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 SARS-CoV-2 antigen-detection rapid diagnostic tests for COVID-19 self-testing/self-sampling with molecular and professional-use tests:a systematic review and meta-analysis

Self-testing is an effective tool to bridge the testing gap for several infectious diseases; however, its performance in detecting SARS-CoV-2 using antigen-detection rapid diagnostic tests (Ag-RDTs) has not been systematically reviewed. This study aimed to inform WHO guidelines by evaluating the accuracy of COVID-19 self-testing and self-sampling coupled with professional Ag-RDT conduct and interpretation. Articles on this topic were searched until November 7th, 2022. Concordance between self-testing/self-sampling and fully professional-use Ag-RDTs was assessed using Cohen’s kappa. Bivariate meta-analysis yielded pooled performance estimates. Quality and certainty of evidence were evaluated using QUADAS-2 and GRADE tools. Among 43 studies included, twelve reported on self-testing, and 31 assessed self-sampling only. Around 49.6% showed low risk of bias. Overall concordance with professional-use Ag-RDTs was high (kappa 0.91 [95% confidence interval (CI) 0.88–0.94]). Comparing self-testing/self-sampling to molecular testing, the pooled sensitivity and specificity were 70.5% (95% CI 64.3–76.0) and 99.4% (95% CI 99.1–99.6), respectively. Higher sensitivity (i.e., 93.6% [95% CI 90.4–96.8] for Ct &lt; 25) was estimated in subgroups with higher viral loads using Ct values as a proxy. Despite high heterogeneity among studies, COVID-19 self-testing/self-sampling exhibits high concordance with professional-use Ag-RDTs. This suggests that self-testing/self-sampling can be offered as part of COVID-19 testing strategies. Trial registration: PROSPERO: CRD42021250706.</p

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