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Abstrackr

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Description

Machine learning-powered tool for prioritizing and screening abstracts in systematic reviews

Market positioning

Specialized tool for abstract screening optimization

Target audience

Systematic review teams and researchers conducting literature reviews

Use cases

Machine learning-powered tool for prioritizing and screening abstracts in systematic reviews

Company information

Company size

Small (estimated 10-50 employees)

Revenue

$1-10M (estimated)

Scale

Niche academic/research market

Number of users

1,000-10,000 (estimated research community)

Features

Abstract screening prioritization, Automated relevance prediction, Collaborative screening, Machine learning algorithms, Workflow optimization

Pricing

Pricing modelFreemium
Starting priceFree (with premium features available)
Billing periodMonthly/Annual (typical for freemium models)

Pros and cons

Based on: (AI summary)

Pros

  • Machine learning prioritization
  • Free tier available
  • Reduces screening workload
  • Academic backing

Cons

  • Limited to abstract screening phase
  • May require validation of ML predictions
  • Less comprehensive than full platforms

Feature Comparison

Top features across 19 competitors (most common first)

Feature Cochrane Da…DistillerSRCore Outcom…CovidenceEPPI-Review…EPPI-Review…Nested Know…Internation…DynaMedJBI-SUMARIAbstrackrGiotto Comp…ACP Journal…LitStreamRobotReview…SWIFT-Activ…SRDB.PROEvidence-Ba…Journal Wat…
Reference management
Manual addition
Data extraction
Data extraction tools
Direct search capability
Living/updatable reviews
Machine learning integration
Collaboration tools
Dual extraction
Public outputs
Quality appraisal features
Screening automation
Data extraction capabilities
Collaboration features
Quality appraisal
Screening tools
Primary care journal review service
Literature screening
Clinical decision support
Cochrane Handbook for systematic review methods