Article type
Year
Abstract
Background:The treatment landscape for rheumatoid arthritis (RA) continues to evolve, and timely, high-quality evidence syntheses are of high interest to clinicians, patients, and policy-makers. To accomplish this, rapid approaches for identifying and classifying trials are required.
Objectives: To develop and pilot a novel approach combining automation and crowd-sourcing to identify and classify all randomised control trials (RCTs) of disease-modifying anti-rheumatic drugs (DMARDs) for RA to inform living network meta-analyses (LNMA).
Methods: A literature search of MEDLINE, EMBASE and Cochrane CENTRAL was conducted using filters for “RA” and “RCTs”. The search results were then uploaded to an RCT Classifier (Wallace 2017), available in the Cochrane Register of Studies (CRS-Web), which uses machine learning algorithms to assign a probability of each citation being a true RCT. Citations with a
Objectives: To develop and pilot a novel approach combining automation and crowd-sourcing to identify and classify all randomised control trials (RCTs) of disease-modifying anti-rheumatic drugs (DMARDs) for RA to inform living network meta-analyses (LNMA).
Methods: A literature search of MEDLINE, EMBASE and Cochrane CENTRAL was conducted using filters for “RA” and “RCTs”. The search results were then uploaded to an RCT Classifier (Wallace 2017), available in the Cochrane Register of Studies (CRS-Web), which uses machine learning algorithms to assign a probability of each citation being a true RCT. Citations with a