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Abstrackr vs SWIFT-Review

Abstrackr

abstrackr.com

SWIFT-Review

sciome.com

Side by side

FigureAbstrackrSWIFT-Review
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WebYes—
Windows—Yes
macOS—Yes
Plans published11
Spec score · Literature Review Software8.0 (#7)7.3 (#25)
Spec score · Systematic Review Software8.0 (#9)7.4 (#25)
Spec score · AI Literature Review Software8.0 (#12)7.3 (#29)
PurposeAbstrackr is a citation-screening tool for systematic reviews.SWIFT-Review is an interactive workbench for problem formulation and literature prioritization in systematic reviews.
SupportThe site directs users to its community forum for methodology questions and workflow discussions.Sciome directs SWIFT-Review inquiries to [email protected].

Plans

Abstrackr

  • FreeFree

Abstrackr pricing →

SWIFT-Review

  • Free desktop softwareFree

SWIFT-Review pricing →

In their own words

Abstrackr

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.

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SWIFT-Review

SWIFT-Review is a desktop workbench for formulating systematic reviews and prioritizing literature for screening. Statistical text mining and machine learning identify over-represented topics and rank documents for manual review. Users can search, categorize, prioritize, visualize, and extract information from large citation sets. The ranking workflow requires an initial training seed, and the software does not estimate how many relevant articles remain above a threshold. It can import PubMed IDs or XML, RIS files, and previously saved SWIFT Text-mining Project files; PMID imports retrieve citation data through NLM’s Entrez e-Utilities interface. Exports include Excel, RIS, and plain tab-delimited text. The free desktop software is available for Windows and Mac, with downloads through Sciome’s applications portal after signing in or creating a free account. Installation requires administrative access. The guide lists 64-bit Windows 7, 8, or 10 and 64-bit Mac OS X 10.8 or later, and recommends 8 GB RAM; very large projects may need more. Minor updates usually happen automatically, while major updates may require administrator privileges.

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