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<https://discovery.ucl.ac.uk/id/eprint/10039588> <http://purl.org/dc/terms/title> "Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10039588> <http://purl.org/ontology/bibo/abstract> "OBJECTIVES: Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed tomake this process more efficient via a hybrid approach using both crowdsourcing andML. METHODS: We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. RESULTS: Combining ML and crowdsourcing provid es a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone. CONCLUSIONS: Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10039588> <http://purl.org/dc/terms/date> "2017-11" .
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