The exponential growth of scientific literature poses increasing challenges for evidence synthesis. Systematic reviews (SRs) usually rely on keyword-based database searches, which are limited by inconsistent terminology and indexing delays. Citation searching-identifying studies that cite or are cited by known relevant articles-offers a complementary route to uncover additional evidence but remains poorly automated and integrated into screening workflows. We developed BibliZap, an open-source, fully automated citation-searching tool built on Lens.org data, performing multi-level forward and backward citation searches with relevance-based ranking. Its performance was evaluated across 66 published SRs, comparing five approaches: (1) PubMed-only searches; (2) PubMed followed by BibliZap restricted to the top 500 ranked results; (3) PubMed followed by full BibliZap screening; and (4-5) two exploratory early-stop strategies where BibliZap was initiated after identifying the first or the first three PubMed relevant records. The primary outcome was sensitivity, with secondary assessments of screening workload and precision. When used after PubMed screening, BibliZap increased mean sensitivity from 75% to 97%, achieving complete recall in over half of the reviews. Screening only the top 500 outputs still allowed over 90% of reviews to reach or exceed 80% recall. BibliZap recovered a median of three additional included articles per review, not retrieved by PubMed, while adding a median of 6,450 additional records. Citation searching via BibliZap enhances the completeness of evidence retrieval in SRs based on restricted database searches and supports transparent, scalable workflows adaptable to rapid and exploratory review contexts.
Bentegeac et al. (Tue,) studied this question.
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