The semi-automation of title and abstract screening: a retrospective exploration of ways to leverage Abstrackr's relevance predictions in systematic and rapid reviews.

ABSTRACT:

Background

We investigated the feasibility of using a machine learning tool's relevance predictions to expedite title and abstract screening.

Methods

We subjected 11 systematic reviews and six rapid reviews to four retrospective screening simulations (automated and semi-automated approaches to single-reviewer and dual independent screening) in Abstrackr, a freely-available machine learning software. We calculated the proportion missed, workload savings, and time savings compared to single-reviewer and dual independent screening by human reviewers. We performed cited reference searches to determine if missed studies would be identified via reference list scanning.

Results

For systematic reviews, the semi-automated, dual independent screening approach provided the best

SUBMITTER: Gates A 

PROVIDER: S-EPMC7268596 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature