Decoding semi-automated title-abstract screening: findings from a convenience sample of reviews.

ABSTRACT:

Background

We evaluated the benefits and risks of using the Abstrackr machine learning (ML) tool to semi-automate title-abstract screening and explored whether Abstrackr's predictions varied by review or study-level characteristics.

Methods

For a convenience sample of 16 reviews for which adequate data were available to address our objectives (11 systematic reviews and 5 rapid reviews), we screened a 200-record training set in Abstrackr and downloaded the relevance (relevant or irrelevant) of the remaining records, as predicted by the tool. We retrospectively simulated the liberal-accelerated screening approach. We estimated the time savings and proportion missed compared with dual independent screening. For reviews with pairwise meta-analyses, we evaluated changes to the po

SUBMITTER: Gates A 

PROVIDER: S-EPMC7694314 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature