Axelium vs Colandr

Axelium is an AI-assisted platform for systematic reviews and meta-analyses, spanning evidence search, screening, analysis, reporting, and living reviews. Its 11 stages include protocol, search, screening, full-text review, extraction, RoB 2.0, GRADE, statistics, network meta-analysis, reporting, and living reviews. The maker says AI extracts structured values while deterministic code calculates statistics, and data points link to snippets in original PDFs. Listed methods include pairwise and network synthesis, Cochrane RoB 2.0, GRADE certainty ratings, CERQual, and PRISMA-style reporting. It imports references from tools and databases including EndNote, Zotero, PubMed, ClinicalTrials.gov, and Web of Science; exports include DOCX, XLSX, RIS, BibTeX, and RevMan. Team controls include roles, comments, dual-person sign-off, conflict adjudication, and versioned audit snapshots. A free plan includes one lifetime review and one seat; Researcher costs $29 a month. Human validation is needed for screening suggestions, extracted values, risk-of-bias assessments, and draft reports. Recurring statistical refresh for living-review network meta-analysis is paused, though scheduled cycles can import, screen, and extract new evidence.

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.