Background: Systematic reviews are critical for obtaining accurate estimates of diagnostic test accuracy, yet theserequire extracting information buried in free text articles, which is often laborious. Objective: We create a datasetdescribing the data extraction and synthesis processes in 63 DTA systematic reviews, and demonstrate its utility byusing it to replicate the data synthesis in the original reviews. Method: We construct our dataset using a customautomated extraction pipeline complemented with manual extraction, verification, and post-editing. We evaluate us-ing manual assessment by two annotators and by comparing against data extracted from source files. Results: Theconstructed dataset contains 5,848 test results for 1,354 diagnostic tests from 1,738 diagnostic studies. We observe anextraction error rate of 0.06–0.3%. Conclusions: This constitutes the first dataset describing the later stages of theDTA systematic review process, and is intended to be useful for automating or evaluating the process.

International audience