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Testing a filtering strategy for systematic reviews: evaluating work savings and recall
Randi Proescholdt1, Tzu-Kun Hsiao1, Jodi Schneider1
1University of Illinois at Urbana-Champaign, Champaign, IL.
Summary
Automated publication type filtering for systematic reviews (SRs) can save significant time, achieving 33.6% work savings and 98.3% recall. Personalizing this strategy is key for efficient article screening.
Area of Science:
- Health Sciences
- Information Science
Background:
- Systematic reviews (SRs) are crucial for evidence synthesis but are labor-intensive.
- Efficiently identifying relevant publications is a major challenge in SRs.
Purpose of the Study:
- To evaluate the work savings and recall of a publication type filtering strategy.
- To assess the effectiveness of machine learning models (Multi-Tagger, web RCT Tagger) for automated filtering in SRs.
Main Methods:
- Retrospective application of a filtering strategy using two machine learning models to 10 drug effectiveness SRs.
- Analysis of work savings and recall based on the models' output compared to included articles.
Main Results:
- The filtering strategy yielded mean work savings of 33.6% and recall of 98.3%.
- Only 7 articles were incorrectly filtered out, with one misclassification due to unspecified publication types.
- Minimal loss of relevant articles was observed.
Conclusions:
- Automated publication type filtering offers substantial work savings in SRs with minimal impact on recall.
- Personalization of filtering strategies and integration with other methods can further enhance efficiency.
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