Abstrackr vs Colandr

Abstrackr is a free web tool for screening citations in systematic reviews. It supports solo work, blinded team screening, pilot rounds, and conflict resolution. After about 100 labels, machine learning ranks remaining citations by predicted relevance. A live PRISMA flow table records screening thresholds, resolved conflicts, exclusion reasons by tag, and analysis of the model’s stop point. Citations can be imported from PubMed, Scopus, or Embase, or uploaded as RIS and CSV files. The service deduplicates records by DOI and PMID and can merge remaining near-duplicates. Results export as CSV or RIS, with a full JSONL download for DuckDB queries. Blinding starts enabled, and changes are logged with user, reason, timestamp, and project snapshots. Member and Leader roles have no stated team-size limit, while reminders arrive 7, 4, and 1 day before deadlines. No installation is needed, and the site directs methodology questions to its community forum.

Colandr is a free web platform for evidence reviews and other document reviews and syntheses. Machine learning ranks citations by relevance to help screen titles and abstracts, while natural language processing classifies included documents into user-defined categories and assists full-text data extraction. Reviewers retain the final inclusion, exclusion, and classification decisions, and teams can collaborate and cross-check work. Citation files can be imported in TXT, RIS, or BIB formats, with a 40 MB limit per import, and screening decisions and extracted data can be exported as CSV. Colandr does not connect directly to citation databases; users export citations elsewhere, although the platform can help create Boolean search strings. Full-text papers must be found and imported individually, with no mass-upload feature. Relevance rankings update after every 10 screened citations, and metadata suggestions begin after 50 hand-coded extractions. Colandr describes itself as open-source and open-access.