Rev Med Chil. 2025 Sep;153(9):641-645. doi: 10.4067/s0034-98872025000900641. Epub 2025 Sep 2.
ABSTRACT
Recently, there has been a surge in technological tools designed to automate tasks across various areas of health sciences, including the identification of evidence used in the development of evidence syntheses that inform clinical practice guideline (CPG) recommendations. Simultaneously, there has been a significant increase in the production of systematic reviews, meaning that much of the relevant evidence is already included in existing reviews.
AIM: To compare the performance of the semi-automated Epistemonikos Evidence Matrix tool with that of a traditional manual literature search in identifying studies for the development of clinical practice guidelines.
MATERIALS AND METHODS: During the development of three CPGs (focused on HIV/AIDS, pediatric asthma management, and stroke management), we compared studies identified through a traditional search strategy in MEDLINE, Embase, and the Cochrane Library with those found using a strategy based on existing systematic reviews, via the Epistemonikos database. The traditional search employed keyword-based strategies and a specific filter for randomized controlled trials. In contrast, the Epistemonikos-based strategy relied on the semi-automated Evidence Matrix tool, which identifies studies shared across two or more systematic reviews.
RESULTS: Across the three guidelines, 8,466 potentially relevant articles were identified using the traditional method, compared to 6,771 using the Epistemonikos-based method. Of these, 155 studies (1.8%) were deemed truly relevant in the traditional search, versus 103 (1.5%) in the Epistemonikos-based approach (p= 0.14). The approach based on existing reviews demonstrated significantly higher precision (94% vs. 78%, p<0.01) but lower sensitivity (58% vs. 88%, p<0.01) compared to the traditional search.
CONCLUSIONS: The evidence search strategy based on existing systematic reviews is an efficient and reliable alternative for identifying relevant studies to support evidence-based decision-making.
PMID:41021846 | DOI:10.4067/s0034-98872025000900641
