Speed and Sensitivity in Deduplication: Evaluating DeDupli Against Established Tools for Systematic Reviews

Abstract Background Duplicate records are a common challenge in systematic reviews, arising from database overlap and multiple citation formats. Failure to address duplicates increases reviewer workload and risks bias. Existing tools such as EndNote and Rayyan offer deduplication features, but evaluations highlight trade-offs between sensitivity and specificity. DeDupli, a novel Streamlit-based, privacy-friendly tool, was developed to balance speed, accuracy, and user oversight through automatic and manual deduplication modes. Methods We generated three synthetic RIS datasets (262, 200, and 320 records) with known duplicate clusters to simulate real-world bibliographic search outputs. Three independent reviewers evaluated deduplication performance across EndNote, Rayyan, and DeDupli (automatic and manual modes) in a crossover design. Reviewers recorded duplicates removed, uniques retained, and processing time. Ground truth was used to calculate true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). Performance metrics included sensitivity, specificity, precision, and F1-score. Results Across 1,564 records with 428 true duplicates, DeDupli achieved perfect sensitivity (1.000), eliminating all duplicates. EndNote and Rayyan achieved perfect specificity and precision (1.000), but each missed 13 duplicates (sensitivity 0.939). DeDupli-Autoprocessed datasets in under 5 seconds but introduced more false positives (precision 0.899, F1 0.947). DeDupli-Manualrequired ~5 minutes, reduced false positives, and improved precision (0.934) and F1 (0.966). By contrast, EndNote (2636 s) and Rayyan (2580 s) required ~40 minutes per dataset. Reviewer-level analysis showed minimal variability, confirming consistency across raters. Conclusion DeDupli demonstrated superior sensitivity and substantial time savings compared with EndNote and Rayyan. Its dual-mode design offers flexibility, allowing users to prioritize either speed (automatic) or accuracy (manual). These results highlight DeDupli as a promising, privacy-friendly solution to streamline deduplication in systematic reviews and meta-analyses. Validation on real-world datasets and integration into review workflows are recommended for future work.