Elicit's structured extraction turning 200 million papers into a comparison table is the capability most people are underusing

The paper discovery with natural language search and AI-generated summaries is what most people know about. The Structured Extraction for comparative tables of study data across multiple papers is the capability that changes the research economics significantly.

Instead of reading twenty papers to extract the methodology, sample size, intervention type and outcome measures from each one, you define the columns you want and Elicit populates the table across all the papers in your search. That is not a minor time saving on a systematic review. It is the difference between a task that takes days and one that takes hours.

The inline citation links to sources alongside the AI summaries are the trust layer that makes the output usable for academic work rather than just informative for personal research. Every claim traces back to a citable source rather than requiring you to go back and verify.

For researchers doing systematic reviews specifically, what is the average number of papers you are typically extracting data from and has the structured extraction accuracy been reliable enough to reduce rather than just accelerate the manual verification step?