Presentation (CTI)

OBJECTIVES: Systematic literature reviews are the gold standard for synthesising research but inevitably become outdated as new developments emerge. As published evidence increases, "living” literature reviews (LLRs), i.e. reviews that continuously update, offer significant value but challenge traditional quality assurance models. The robustness and mechanisms of quality assurance for LLRs have not been fully established. This research aimed to explore methods and quality assurance mechanisms in recently published LLRs.
METHODS: Ad-hoc scoping searches were conducted using Google Scholar to identify active, open-access LLRs published in the last 10 years. Methods and discussion sections of LLRs were reviewed to determine the common practices in LLR design, typical limitations, and quality assurance mechanisms.
RESULTS: Seven LLRs were identified across various disease areas (COVID-19, Zika virus, traumatic brain injury, prostate cancer, myopia, and osteoporosis). The most utilised literature search platforms were PubMed, OVID, and LILACS, all of which have auto-alert functions. Overall, three LLRs automatically reran searches on a regular basis, with reviewers assessing new evidence weekly or monthly. Where reported, publication updates ranged from weekly to biannually. The necessity of updates depended on new evidence availability, quality, and the impact of new findings on study outcomes and decision-making. One study reported the partial automation of screening and automated updates of tables and figures. LLR-specific limitations and quality assurance mechanisms were rarely reported. Risk of bias was always assessed manually, often using GRADE, and most studies manually extracted and incorporated newly published evidence.
CONCLUSIONS: LLRs provide up-to-date information in rapidly evolving fields. Overall methodology followed that of traditional reviews, with most review steps done manually, though automation was occasionally used. Given the increase in AI tool availability and versatility, LLR updates will likely gradually shift to a mostly automated process. Clear quality assurance frameworks are needed to ensure review quality is maintained.