I'm creating a living systematic review. Guide me: 1) Set up infrastructure for continuous updates

Prompt
I'm creating a living systematic review. Guide me: 1) Set up infrastructure for continuous updates
MistralMistral AI
I'm creating a living systematic review. Guide me: 1) Set up infrastructure for continuous updates

Creating a living systematic review (LSR) involves setting up an infrastructure that allows for continuous updates and integration of new evidence. Here’s a step-by-step guide to help you set up the necessary infrastructure:

1. Define the Scope and Objectives


  • Research Question: Clearly define the research question or objectives of your systematic review.

  • Inclusion/Exclusion Criteria: Establish criteria for including and excluding studies.

  • Search Strategy: Develop a comprehensive search strategy to identify relevant studies.


2. Assemble a Team


  • Core Team: Include experts in systematic reviews, information specialists, and subject matter experts.

  • Roles and Responsibilities: Clearly define roles such as search coordinator, data extractor, and quality assessor.


3. Set Up a Document Management System


  • Reference Management Software: Use tools like EndNote, Zotero, or Mendeley to manage references.

  • Data Extraction Tools: Utilize software like Covidence, Rayyan, or DistillerSR for data extraction and management.

  • Version Control: Implement version control systems like GitHub for managing updates and changes.


4. Develop a Search and Monitoring Strategy


  • Automated Alerts: Set up automated alerts in databases like PubMed, Embase, and Cochrane Library to receive updates on new publications.

  • Regular Searches: Schedule regular manual searches to ensure no relevant studies are missed.

  • Grey Literature: Include sources of grey literature such as conference proceedings, dissertations, and preprint servers.


5. Establish a Workflow for Continuous Updates


  • Screening: Develop a protocol for screening new studies for relevance.

  • Data Extraction: Create standardized forms for data extraction.

  • Quality Assessment: Use tools like the Cochrane Risk of Bias Tool or the Newcastle-Ottawa Scale for quality assessment.

  • Data Synthesis: Plan for regular updates to the data synthesis, including meta-analysis if applicable.


6. Implement Automation Tools


  • Automated Screening: Use machine learning tools like ASReview or RobotReviewer for automated screening.

  • Automated Data Extraction: Utilize natural language processing (NLP) tools for automated data extraction.

  • Automated Reporting: Implement tools for automated reporting and updating of the systematic review.


7. Ensure Transparency and Reproducibility


  • Protocol Registration: Register your protocol with PROSPERO or a similar registry.

  • Documentation: Maintain detailed documentation of all processes, including search strategies, inclusion/exclusion criteria, and data extraction methods.

  • Open Access: Consider making your data and methods openly accessible to ensure transparency and reproducibility.


8. Plan for Regular Updates and Publication


  • Update Schedule: Establish a regular update schedule (e.g., quarterly, annually).

  • Publication Plan: Plan for regular publication of updates in journals or repositories that support living systematic reviews.

  • Communication: Develop a communication plan to disseminate updates to stakeholders, including clinicians, policymakers, and the public.


9. Monitor and Evaluate


  • Performance Metrics: Track performance metrics such as the number of new studies included, time taken for updates, and user feedback.

  • Continuous Improvement: Regularly review and improve your processes based on feedback and performance metrics.


10. Engage Stakeholders


  • Stakeholder Involvement: Engage stakeholders in the planning and execution phases to ensure the review meets their needs.

  • Feedback Loop: Establish a feedback loop to gather input from stakeholders and incorporate it into the review process.


Tools and Resources


  • Reference Management: EndNote, Zotero, Mendeley

  • Data Extraction and Management: Covidence, Rayyan, DistillerSR

  • Version Control: GitHub

  • Automated Alerts: PubMed, Embase, Cochrane Library

  • Automated Screening: ASReview, RobotReviewer

  • Protocol Registration: PROSPERO


By following these steps, you can create a robust infrastructure for a living systematic review that ensures continuous updates and integration of new evidence.

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Analysis

This comparison demonstrates the different approaches each AI model takes when responding to the same prompt. Here are the key differences observed:

Response Characteristics

Mistral: Provides a detailed response with 42 sentences.

Key Takeaways

  • Each model brings unique strengths to this type of query
  • Response styles vary significantly between models
  • Consider your specific use case when choosing between these models

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This comparison was generated using the SNEOS AI Comparison ToolPublished: October 15, 2025 | Models: Mistral