# Turn scientific papers into an evidence brief

> Find papers, extract evidence, compare claims, and produce a concise research brief with confidence levels.

- Canonical: https://gptnavi.com/workflows/literature-review-to-evidence-brief
- Category: Research
- Difficulty: Intermediate
- Setup time: 2 hours
- Estimated time saved: 4-10 hours
- Last materially updated: 2026-08-24
- Best for: Researchers, Consultants, Health teams, B2B writers
- Tools: Elicit, Consensus, SciSpace, NotebookLM, ChatGPT

## Quick answer

This workflow is useful when a decision needs more than a web summary and should be grounded in research papers.

## When to use it

- Evidence briefs
- Market reports
- Healthcare content review
- Technical whitepaper research

## Steps

1. **Frame the research question** — Write a narrow question, inclusion criteria, geography, time range, and what decision the answer should support. Tool: NotebookLM. Expected output: A scoped research question.
2. **Find candidate papers** — Search for relevant papers and filter by study type, date, sample, and relevance. Tool: Elicit. Expected output: A shortlist of useful papers.
3. **Check consensus and disagreement** — Compare claims across papers and identify where evidence agrees, conflicts, or stays weak. Tool: Consensus. Expected output: A claim-level evidence map.
4. **Extract methods and caveats** — Use paper reading tools to pull methods, limitations, populations, and measurement details. Tool: SciSpace. Expected output: Evidence notes with caveats.
5. **Write the brief** — Ask AI to synthesize findings with confidence levels, caveats, and implications for the decision. Tool: ChatGPT. Expected output: A decision-ready evidence brief.

## Prompt templates

### Evidence synthesis

Create an evidence brief from these paper notes. Include answer, confidence level, evidence table, caveats, disagreements, and practical implications. Notes: [paste]

### Claim checker

Review these claims against the evidence notes. Mark each claim as supported, partially supported, unsupported, or needs expert review. Claims and notes: [paste]

## Common mistakes

- Treating one paper as consensus
- Ignoring sample size and population differences
- Removing caveats to make the conclusion sound cleaner

## Related workflows

- https://gptnavi.com/workflows/research-inbox-to-weekly-insight-brief
- https://gptnavi.com/workflows/research-notes-to-knowledge-base
- https://gptnavi.com/workflows/research-a-niche-market-with-ai