Citationchaser: A tool for transparent and efficient forward and backward citation chasing in systematic searching
Open accessAbstract
Systematic searching aims to find all possibly relevant research from multiple sources, the basis for an unbiased and comprehensive evidence base. Along with bibliographic databases, systematic reviewers use a variety of additional methods to minimise procedural bias. Citation chasing exploits connections between research articles to identify relevant records for a review by making use of explicit mentions of one article within another. Citation chasing is a popular supplementary search method because it helps to build on the work of primary research and review authors. It does so by identifying potentially relevant studies that might otherwise not be retrieved by other search methods; for example, because they did not use the review authors' search terms in the specified combinations in their titles, abstracts, or keywords. Here, we briefly provide an overview of citation chasing as a method for systematic reviews. Furthermore, given the challenges and high resource requirements associated with citation chasing, the limited application of citation chasing in otherwise rigorous systematic reviews, and the potential benefit of identifying terminologically disconnected but semantically linked research studies, we have developed and describe a free and open source tool that allows for rapid forward and backward citation chasing. We introduce citationchaser , an R package and Shiny app for conducting forward and backward citation chasing from a starting set of articles. We describe the sources of data, the backend code functionality, and the user interface provided in the Shiny app.
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Forward and/or backward citation quality checks can be automated through tools such as SR-Accelerator's Spidercite and Citation Chaser.
“This can be automated, for example, through the use of SR-Accelerator’s Spidercite [14], Citation Chaser [19].”
From The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses (2024) · cites this paper as [19]
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Cited by
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Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
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“The reference lists of all included articles were checked using the web application Citation Chaser (11) , searching for both the citations used by these studies and the articles that cited them.”
- Authors
- Haddaway, N. R., Grainger, M. J., & Gray, C. T.
- Affiliations
- University of Johannesburg (ZA)
- Norwegian Institute for Nature Research (NO)
- Newcastle University (GB)
- Corresponding author
- Haddaway, Neal R.
- Venue
- Research Synthesis Methods
- Publisher
- Wiley
- Published
- 2022-05-07
- Volume
- 13
- Issue
- 4
- Pages
- 533-545
- ISSN
- 1759-2879 (ISSN-L)
- 1759-2887
- OpenAlex
- W4224987102
- Cited by
- 604
- References
- 3
- Citations by year
- 2026: 182 · 2025: 182 · 2024: 133 · 2023: 86 · 2022: 19 · 2021: 1
- FWCI
- 64.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Research Synthesis Methods (publishedVersion, cc-by)
- PubMed (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- systematic reviews, bibliographic databases, search strategies, R package, Shiny app, free and open source software
- MeSH terms
- Systematic Reviews as Topic, Research Design, Databases, Bibliographic, Information Storage and Retrieval
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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