> For the complete documentation index, see [llms.txt](https://docs.owkin.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.owkin.com/what-you-can-do-with-k-pro/literature-review/review-and-synthetize-literature.md).
# Review & synthetize literature
**What to do:** Be specific about the gene, indication, and timeframe. Vague prompts like "tell me about TP53" return overly broad results.
**Which agent/skill:** Consensus (AI-powered scientific search engine)
**Example prompt:**
> Summarize the latest PubMed publications on the role of TP53 mutations in non-small cell lung cancer. Focus on findings from the last 3 years and highlight any consensus on prognostic significance.
**Expected result:** A structured summary of key publications with citations, organized by main findings and areas of consensus/controversy.
**Follow-up:**
> Are there any conflicting findings across these studies regarding TP53's role as a predictive biomarker for immunotherapy response in NSCLC?
**Second example — searching for publications about a drug target:**
> Find publications investigating TROP2 as a therapeutic target in triple-negative breast cancer. Include any data on TROP2 expression levels and their correlation with clinical outcomes.
*Expected result:* A curated list of relevant publications with key findings, expression data, and clinical correlations.
*Follow-up:*
> Based on these publications, what is the evidence for using TROP2 expression as a patient selection biomarker for ADC therapies?
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.owkin.com/what-you-can-do-with-k-pro/literature-review/review-and-synthetize-literature.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
