📊 Elicit#
Overview#
Elicit is an AI-powered research assistant that goes beyond Consensus’s quick synthesis by extracting custom data from papers and helping you screen large volumes of literature. While Consensus answers “what’s the consensus?”, Elicit answers “what specific data do I need from these papers?”
Website: https://elicit.com/
Key Difference from Consensus:
Consensus: Quick synthesis → “Do X interventions work?” → Yes/No with evidence
Elicit: Data extraction → “Extract sample sizes, methods, outcomes from 100 papers” → Custom table ready for analysis
Key Features#
📊 Custom Data Extraction: Define any column, AI extracts from papers
🔍 Bulk Screening: Process 50-200+ papers efficiently
📈 Analysis Tables: Structured data ready for meta-analysis
📝 Paper Summaries: AI-generated abstracts and key findings
🎯 Question Answering: Chat with individual papers
💾 Export: CSV/BibTeX with all extracted data
🆓 Free Tier: 5,000 credits/month (~50-100 papers)
Getting Started#
Visit https://elicit.com/ and sign up (free: 5,000 credits/month, paid plans for heavy users).
Main Use Case: Custom Data Extraction#
Perfect for: Meta-analysis, systematic reviews, literature synthesis requiring structured data.
Example: Extract RCT Details#
Search: “randomized controlled trials cognitive behavioral therapy depression”
Add Custom Columns: Click “+ Add column”
“Sample size?”
“Intervention duration?”
“Control condition?”
“Primary outcome measure?”
“Effect size (Cohen’s d)?”
“Dropout rate?”
AI Extracts: Elicit reads papers and fills columns automatically
Review & Export: Check accuracy, download CSV for meta-analysis
Common Extraction Templates:
Medical Research:
- "Inclusion/exclusion criteria?"
- "Primary/secondary outcomes?"
- "Adverse events?"
- "Funding source?"
ML/AI Research:
- "Dataset used?"
- "Model architecture?"
- "Evaluation metrics?"
- "Code available?"
Social Science:
- "Sample demographics?"
- "Data collection method?"
- "Statistical approach?"
- "Effect sizes?"
Integration with Review Buddy/Findpapers#
Recommended Workflow:
# Step 1: Comprehensive search with Review Buddy
from paper_searcher import PaperSearcher
searcher = PaperSearcher(config)
papers = searcher.search_all(query="your systematic query")
# Step 2: Extract DOIs for Elicit
dois = [p['doi'] for p in papers if p.get('doi')]
with open('dois_for_elicit.txt', 'w') as f:
f.write('\n'.join(dois))
# Step 3: Upload to Elicit & extract custom data
# (Manual step in web interface)
# Step 4: Download structured CSV
# Ready for analysis!
When to Use: Elicit vs Consensus#
Use Case |
Tool |
Why |
|---|---|---|
“What’s the consensus on X?” |
Consensus |
Quick yes/no synthesis |
“Extract sample sizes from 100 RCTs” |
Elicit |
Custom data extraction |
“Does treatment A work?” |
Consensus |
Evidence overview |
“Compare methods across 50 papers” |
Elicit |
Structured comparison |
“Quick background research” |
Consensus |
Fast synthesis |
“Meta-analysis data collection” |
Elicit |
Detailed extraction |
“Teaching/learning” |
Both |
Consensus for overview, Elicit for depth |
Combined Workflow:
1. Consensus (15 min): Get overview and consensus
2. Review Buddy (30 min): Comprehensive search
3. Elicit (2-4 hours): Extract data and screen papers
4. Traditional reading: Verify and deep read
Tips for Success#
Crafting Extraction Questions:
✅ Specific: “What was the mean age of participants?”
❌ Vague: “Participant demographics?”
✅ Closed: “Was the study randomized? (Yes/No)”
❌ Open: “Describe the study design”
Best Practices:
Always verify critical data (sample sizes, effect sizes, p-values)
Check AI citations (click to see source text)
Start with 10-20 papers to test extraction quality
Define all columns before processing to save credits
Warning
AI extractions are ~80-90% accurate. Always verify for publication-quality work!
Resources#
📖 Blog - Tips and use cases