Screen studies for a systematic review with a chat AI
KINDA — AI can help, with a person in the loop.
1 method on the page: 1 agent.
Use cases
Methods
Train a chat AI to screen abstracts against your review criteria
Steps
- Upload inclusion/exclusion criteria as a PDF.
- The agent summarizes the criteria in its own words.
- The researcher reviews and corrects the summary in a single pass.
- The agent re-summarizes until accurate.
- Upload a sample set of abstracts as a training set.
- The agent classifies each abstract as include or exclude with a justification.
- The researcher reviews the results, provides corrective feedback, and supplies targeted examples. Steps 6 and 7 repeat until the agent's classifications are consistent with the researcher's judgment.
- Optional: Title and abstract prompt from the article: "Based on the uploaded criteria, should the following study be included or excluded? Please explain your reasoning."
- Optional: Full-text prompt from the article: "Assess whether this full-text article meets the inclusion criteria for this systematic review. If you decide to include or exclude, provide direct quotations from the article to justify your decision."
- Note: Steps and prompts quoted from the open-access tutorial by Qiu, Jiang, Chen and Lin (CC BY 4.0). A person still makes the final include or exclude call.
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--- name: "cambridge-llm-screening-training-loop-method" description: "Use when you need to Screen studies for a systematic review with a chat AI. Train a chat AI to screen abstracts against your review criteria" --- # Train a chat AI to screen abstracts against your review criteria Job: [Screen studies for a systematic review with a chat AI](https://aiagents.org/canai/cambridge-llm-screening-training-loop) ## Need - AI agent ## Steps 1. Upload inclusion/exclusion criteria as a PDF. 2. The agent summarizes the criteria in its own words. 3. The researcher reviews and corrects the summary in a single pass. 4. The agent re-summarizes until accurate. 5. Upload a sample set of abstracts as a training set. 6. The agent classifies each abstract as include or exclude with a justification. 7. The researcher reviews the results, provides corrective feedback, and supplies targeted examples. Steps 6 and 7 repeat until the agent's classifications are consistent with the researcher's judgment. 8. Optional: Title and abstract prompt from the article: "Based on the uploaded criteria, should the following study be included or excluded? Please explain your reasoning." 9. Optional: Full-text prompt from the article: "Assess whether this full-text article meets the inclusion criteria for this systematic review. If you decide to include or exclude, provide direct quotations from the article to justify your decision." 10. Note: Steps and prompts quoted from the open-access tutorial by Qiu, Jiang, Chen and Lin (CC BY 4.0). A person still makes the final include or exclude call. ## Credit Method author Tongyu Qiu et al., International Journal of Technology Assessment in Health Care (CC BY 4.0) Source: https://www.cambridge.org/core/journals/international-journal-of-technology-assessment-in-health-care/article/promptbased-tutorial-for-large-language-modelassisted-screening-in-systematic-reviews-and-metaanalyses/A769118FCF9B9A1F0E9911E770044CE1
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How you would do this use case, in plain words. Anyone can post.
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A bad request returns JSON like {"error":"Method description is required"} with status 400.
| Field | What it is |
|---|---|
jobId | This use case. Review queue uses useCaseTitle + email instead. |
summary | Method description. Shown on the card. Also accepts title. |
products | Optional. Array of { name, url }. Legacy: tools. |
prompts | Optional. Array of prompt strings. Legacy: one prompt string. |
skills | Optional. Array of { text, url }. Legacy: skill + skillUrl. |
authorName | Author name. Legacy live field: postedBy. |
author | Optional 𝕏 handle. Legacy live field: creditHandle. |
parts | Optional. Inferred from products, prompts, and skills if omitted.standalone-product, ai-agent, ai-tool, skill, prompt. Do not send a prompt/product/agent/skill radio. |
{
"jobId": "cambridge-llm-screening-training-loop",
"summary": "Turns a Zoom recording into notes and action items with owners",
"products": [
{
"name": "Claude",
"url": "https://claude.ai"
}
],
"prompts": [
"Turn this transcript into notes and action items with owners."
],
"skills": [
{
"text": "Watch a folder. When a transcript lands, write notes with owners.",
"url": "https://example.com/zoom-notes.md"
}
],
"authorName": "Alex Rivera",
"author": "@handle"
}