Comparison of a traditional systematic review approach with review-of-reviews and semi-automation as strategies to update the evidence

Shivani M Reddy,Sheila Patel,Meera Viswanathan,Meghan Weyrich,Joshua Fenton

doi:10.1186/s13643-020-01450-2

Shivani M Reddy, Sheila Patel + Show 3 more

Open Access

https://doi.org/10.1186/s13643-020-01450-2

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Abstract

BackgroundThe exponential growth of the biomedical literature necessitates investigating strategies to reduce systematic reviewer burden while maintaining the high standards of systematic review validity and comprehensiveness.MethodsWe compared the traditional systematic review screening process with (1) a review-of-reviews (ROR) screening approach and (2) a semi-automation screening approach using two publicly available tools (RobotAnalyst and AbstrackR) and different types of training sets (randomly selected citations subjected to dual-review at the title-abstract stage, highly curated citations dually reviewed at the full-text stage, and a combination of the two). We evaluated performance measures of sensitivity, specificity, missed citations, and workload burdenResultsThe ROR approach for treatments of early-stage prostate cancer had a poor sensitivity (0.54) and studies missed by the ROR approach tended to be of head-to-head comparisons of active treatments, observational studies, and outcomes of physical harms and quality of life. Title and abstract screening incorporating semi-automation only resulted in a sensitivity of 100% at high levels of reviewer burden (review of 99% of citations). A highly curated, smaller-sized, training set (n = 125) performed similarly to a larger training set of random citations (n = 938).ConclusionTwo approaches to rapidly update SRs—review-of-reviews and semi-automation—failed to demonstrate reduced workload burden while maintaining an acceptable level of sensitivity. We suggest careful evaluation of the ROR approach through comparison of inclusion criteria and targeted searches to fill evidence gaps as well as further research of semi-automation use, including more study of highly curated training sets.

Highlights

The exponential growth of the biomedical literature necessitates investigating strategies to reduce systematic reviewer burden while maintaining the high standards of systematic review validity and comprehensiveness
We identified three approaches to updating systematic evidence review (SR): the traditional search and screening method recommended by the Effective Health Care (EHC) Program, a “review of reviews” (ROR) approach, and, semi-automation of abstract screening
With respect to semi-automation approaches, we evaluate training sets composed of randomly selected citations from the traditional database search, highly curated citations identified from full-text review, and a combination of the two to examine ways reviewers can practically incorporate machine learning (ML) tools into the review workflow

Summary

Introduction

The exponential growth of the biomedical literature necessitates investigating strategies to reduce systematic reviewer burden while maintaining the high standards of systematic review validity and comprehensiveness. We examine strategies to reduce workload burden of title and abstract screening as an adjunct investigation in parallel to conducting a traditional update SR on treatments for early-stage prostate cancer. We relied on two recent SRs of treatments for early-stage prostate cancers: one focused on comparisons of active treatment to conservative management [7], and the second included head-to-head comparisons of active treatments [8] These reviews were conducted in 2014 and 2016, necessitating a SR update. Semi-automation screening software uses text-mining algorithms to find patterns in unstructured text and machine learning (ML) to train predictive classification algorithms to make inclusion and exclusion decisions or prioritize relevant citations in the title and abstract screening step of an SR [9]. Active learning is a type of ML where the algorithm and reviewer interact: the algorithm generates a list of prioritized citations for the researcher to review rather than presenting unscreened citations in a random order; the step of reviewer inclusion and exclusion decisions further train the predictive model [11]

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