BACKGROUND: Filtering the deluge of new research to facilitate evidence synthesis has proven to be unmanageable using current paradigms of search and retrieval. Crowdsourcing, a way of harnessing the collective effort of a 'crowd' of people, has the potential to support evidence synthesis by addressing this information overload created by the exponential growth in primary research outputs. Cochrane Crowd, Cochrane's citizen science platform, offers a range of tasks aimed at identifying studies related to healthcare. Accompanying each task are brief, interactive training modules and agreement algorithms that help ensure accurate collective decision-making. OUR OBJECTIVES WERE: (1) to evaluate the performance of Cochrane Crowd in terms of its accuracy, capacity and autonomy; and (2) to examine contributor engagement across three tasks aimed at identifying randomised trials. STUDY DESIGN: Crowd accuracy was evaluated by measuring the sensitivity and specificity of crowd screening decisions on a sample of titles and abstracts, compared with 'quasi gold-standard' decisions about the same records using the conventional methods of dual screening. Crowd capacity, in the form of output volume, was evaluated by measuring the number of records processed by the crowd, compared with baseline. Crowd autonomy, the capability of the crowd to produce accurate collectively-derived decisions without the need for expert resolution, was measured by the proportion of records that needed resolving by an expert. RESULTS: The Cochrane Crowd community currently has 18,897 contributors from 163 countries. Collectively, the Crowd has processed 1,021,227 records, helping to identify 178,437 reports of randomised trials (RCTs) for Cochrane's Central Register of Controlled Trials. The sensitivity for each task was 99.1% for the randomised controlled trial identification task (RCT ID), 99.7% for the randomised controlled trial identification task of trial from ClinicalTrials.gov (CT ID) and 97.7% for identification of randomised controlled trials from the International Clinical Trials Registry Platform (ICTRP ID). The specificity for each task was 99% for RCT ID, 98.6% for CT ID and 99.1% for ICTRP ID. The capacity of the combined Crowd and machine learning workflow has increased five-fold in six years, compared with baseline. The proportion of records requiring expert resolution across the tasks ranged from 16.6% to 19.7%. CONCLUSION: Cochrane Crowd is sufficiently accurate and scalable to keep pace with the current rate of publication (and registration) of new primary studies. It has also proved to be a popular, efficient and accurate way for a large number of people to play an important voluntary role in health evidence production. Cochrane Crowd is now an established part of Cochrane's effort to manage the deluge of primary research being produced.
J Clin Epidemiol
Citizen science, Cochrane, Crowdsourcing, Embase, Evidence production, Human intelligence tasking, Information management, Machine learning, Randomised controlled trial, Screening, Systematic review