Trialstreamer: A living, automatically updated database of clinical trial reports

Abstract

Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer continuously monitors PubMed and the World Health Organization International Clinical Trials Registry Platform, looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial PICO (populations, interventions/comparators, and outcomes) elements and map these snippets to normalized MeSH (Medical Subject Headings) vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database, which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of early June 2020, we have indexed 673 191 publications of RCTs, of which 22 363 were published in the first 5 months of 2020 (142 per day). We additionally include 304 111 trial registrations from the International Clinical Trials Registry Platform. The median trial sample size was 66. Conclusions We present an automated system for finding and categorizing RCTs. This yields a novel resource: a database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (https://trialstreamer.robotreviewer.net).

Data extraction methods for systematic review (semi)automation: A living systematic review

“…27 Further metrics included under ‘Other’ were odds ratios, 37 normalised discounted cumulative gain, 29 ‘sentences needed to screen per article’ in order to find one relevant sentence, 38 McNemar test, 36 C-statistic (with 95% CI) and Brier score (with 95% CI). 39 …”

State of the evidence: a survey of global disparities in clinical trials

“…We developed a machine learning system which maintains a live database of annotated RCT reports, named Trialstreamer. We have described the computational methods and accuracy of the system components in detail elsewhere, 11 and summarise the key points relevant to the current study below.…”

“…org) a collaborative citizen science project. We do not rely on the Publication Type index alone, as we have previously found it to miss a substantial proportion of the 5-7 most recent years of articles 11 (due to delay in manual indexing after publication). We next removed any RCTs that were not conducted in humans (eg, animal or agricultural studies) using an SVM model.…”

State of the evidence: a survey of global disparities in clinical trials

“…[14] These machine learning models are trained 1 on 280,000 abstracts manually labelled as being RCTs or not by Cochrane Crowd, 2 a collaborative citizen science project. We do not rely on the Publication Type index alone, as we have previously found it to miss a substantial proportion of the 5-7 most recent years of articles [11] (due to delay in manual indexing after publication). We next removed any RCTs that were not conducted in humans (e.g., animal or agricultural studies) using an SVM model.…”

“…We developed a machine learning system which maintains a live database of annotated RCT reports, named Trialstreamer. We have described the computational methods and accuracy of the system components in detail elsewhere, [11] and summarize the key points relevant to the current study below.…”