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In BMC medical research methodology

BACKGROUND : Standard practice for conducting systematic reviews (SRs) is time consuming and involves the study team screening hundreds or thousands of citations. As the volume of medical literature grows, the citation set sizes and corresponding screening efforts increase. While larger team size and alternate screening methods have the potential to reduce workload and decrease SR completion times, it is unknown whether investigators adapt team size or methods in response to citation set sizes. Using a cross-sectional design, we sought to understand how citation set size impacts (1) the total number of authors or individuals contributing to screening and (2) screening methods.

METHODS : MEDLINE was searched in April 2019 for SRs on any health topic. A total of 1880 unique publications were identified and sorted into five citation set size categories (after deduplication): < 1,000, 1,001-2,500, 2,501-5,000, 5,001-10,000, and > 10,000. A random sample of 259 SRs were selected (~ 50 per category) for data extraction and analysis.

RESULTS : With the exception of the pairwise t test comparing the under 1000 and over 10,000 categories (median 5 vs. 6, p = 0.049) no statistically significant relationship was evident between author number and citation set size. While visual inspection was suggestive, statistical testing did not consistently identify a relationship between citation set size and number of screeners (title-abstract, full text) or data extractors. However, logistic regression identified investigators were significantly more likely to deviate from gold-standard screening methods (i.e. independent duplicate screening) with larger citation sets. For every doubling of citation size, the odds of using gold-standard screening decreased by 15 and 20% at title-abstract and full text review, respectively. Finally, few SRs reported using crowdsourcing (n = 2) or computer-assisted screening (n = 1).

CONCLUSIONS : Large citation set sizes present a challenge to SR teams, especially when faced with time-sensitive health policy questions. Our study suggests that with increasing citation set size, authors are less likely to adhere to gold-standard screening methods. It is possible that adjunct screening methods, such as crowdsourcing (large team) and computer-assisted technologies, may provide a viable solution for authors to complete their SRs in a timely manner.

O’Hearn Katie, MacDonald Cameron, Tsampalieros Anne, Kadota Leo, Sandarage Ryan, Jayawarden Supun Kotteduwa, Datko Michele, Reynolds John M, Bui Thanh, Sultan Shagufta, Sampson Margaret, Pratt Misty, Barrowman Nick, Nama Nassr, Page Matthew, McNally James Dayre

2021-Jul-08

Crowdsourcing, Machine learning, Scoping reviews, Systematic reviews