Study design and data source

We searched all systematic reviews conducted by an evidence-based practice center in the United States. The evidence-based practice center is one of the 12 evidence-based practice centers designated and funded by U.S. Agency for Healthcare Research and Quality (AHRQ). It specializes in conducting systematic reviews and meta-analysis, and developing clinical practice guidelines, evidence dissemination and implementation tools, and related methodological research. Eligible systematic reviews had to 1) be started and finished between June 1, 2010 and Dec 31, 2017; 2) follow standard systematic review procedures [5]: 1) dual independent screening of abstracts and titles, abstract inclusion by one reviewer prompted automatic inclusion for full text screening; 2) dual independent screening of full text, disagreements between reviewers reconciled via consensus or arbitration by a third reviewer. The final included list of studies consisted of the studies after abstract screening, and full texting screening.; 3) use a web-based commercial systematic review software (DistillerSR, Evidence Partners Incorporated, Ottawa, Canada); and 4) be led by at least one of the core investigators of the evidence-based practice center. The investigation team consisted of a core group (10–15 investigators at any time period) and external collaborators with either methodological or content expertise. DistillerSR was used to facilitate abstracts and full texts screening and track all inclusion and exclusion decisions made by human reviewers. We did not use any automation algorithm in the included systematic reviews.

Outcomes

The main outcome of interest was error rate of human reviewers during abstract screening. An error was defined as a decision made by a single reviewer in abstract screening that was inconsistent (i.e., false inclusion or false exclusion) with the final included list of studies that that underwent abstract screening, and full texting screening and were eligible for data extraction and analysis (see Fig 1). We calculated error rate as the number of errors divided by the total number of screened abstracts (the total number of citations*2). We also estimated the overall abstract inclusion rate (defined as the number of eligible studies after abstract screening divided by the total number of citations), and the final inclusion rate (defined as the number of the final included studies divided by the total number of citations). In this study, we did not compare the performance between human reviewers and the automation algorithms integrated in DistillerSR.

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Fig 1. Errors occurred during systematic review abstract screening.

https://doi.org/10.1371/journal.pone.0227742.g001

Statistical analysis

We calculated the outcomes of interest for each eligible systematic review. The mean of the outcomes across systematic reviews were the average outcomes of each study weighted by the inverse proportion to the variance of the denominator (total number of screened abstracts or total number of citations). The variance was estimated using the following formula: xbar = weight mean

All analyses were implemented with Stata version 15.1 (StataCorp LP, College Station, TX, USA).

Implications

A rigorous systematic review follows strict approaches and requires significant resource and time to complete, which typically lasts 6–18 months by a team of human reviewers.[25] Automation tools have the potential to mimic human activities in systematic review tasks and gained popularity in academia and industry. However, validity of the automation tools has yet to be established. [19, 24] It is intuitive to assume that these tools should achieve a zero error rate in order to be implemented to generate evidence used for decision-making (i.e., 100% sensitivity and 100% specificity).

Human reviewers have been used as the “gold standard” in evaluating the automation tools. However, similar to those “gold standards” used in clinical medicine, 100% accuracy is unlikely in reality. We found 10.76% error rate made by human reviewers in abstract screening (an error about 1 in 9 abstracts). This error rate also varied from topics and types of questions. Thus, when developing and refining an automation tool, achieving error rates similar to humans may be adequate. If this is the case, then these tools can serve as a single reviewer that gets paired with a second human reviewer.

Limitations

The sample size is relatively small, especially as we further stratify by clinical areas. The findings may not be generalizable to other systematic review questions or topics. The human reviewers who conducted these systematic reviews had a wide range of content knowledge and methodological experience (from minimum 1 year to over 10 years), which can be quite different from other review teams. In our practice, citations from abstract screening were automatically included when conflicts between two independent reviewers emerged. The abstract inclusion rate and final inclusion rate resulting from this approach can be higher than those of the teams who resolve conflicts in abstract screening. When both reviewers agree on excluding an abstract, this abstract disappears from the process; thus, a dual erroneous exclusion cannot be assessed. We were not able to evaluate error rate during full text screening as we did not track the conflicts between reviewers. Lastly, while we call judgments in study selection that are inconsistent with the final inclusion as errors, we acknowledge that these errors could be due to poor reporting and insufficient data provided in the published abstract. Thus, they may not be avoidable and they are not the fault of human reviewers. In summary, this study is an initial step to evaluate human errors in systematic reviews. Future studies need to evaluate different systematic review approaches (e.g., rapid systematic review, scoping review), clinical areas, and review questions. It is also important to increase the number of systematic reviews involved in the evaluation and include other EPC or non-EPC institutions.