Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Systematic reviews, 2020-10, Vol.9 (1), p.243-243, Article 243

Ort / Verlag

London: BioMed Central

Erscheinungsjahr

2020

Link zum Volltext

Quelle

SpringerNature Journals

Beschreibungen/Notizen

• Abstract Background 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. Methods We 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 burden Results The 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). Conclusion Two 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.