Model Name: Research Question Refinement
Version: 1.0
Overview #
The Research Question Refiner leverages LLMs to assist users in crafting research questions that meet the recommendations for systematic literature reviews (SLRs). The tool performs the following tasks:
- Evaluates whether a research question adheres to established SLR criteria for clarity, focus, and relevance.
- Provides actionable feedback and follow-up questions to guide refinement.
- Suggests modifications to improve alignment with SLR best practices.
This iterative process helps users create research questions optimized for systematic review workflows.
Intended Use #
- Primary Purpose: Facilitate the creation of high-quality research questions for systematic reviews.
- Intended Users: Researchers, healthcare analysts, and systematic review teams.
Evaluation #
- Qualitative Nature of Performance:
- The effectiveness of the tool’s refinements is subjective and depends on the user’s research context, precision of their research question, and expertise.
- Performance is assessed qualitatively through user feedback, as there are no standard metrics to quantify research question quality.
Ethical Considerations #
- Human Oversight: The tool complements, rather than replaces, expert judgment. Users are responsible for evaluating and adopting the tool’s suggestions.
- Bias in GPT-4o: As the underlying model is an OpenAI LLM, its feedback may reflect the biases or limitations inherent to large language models.
Limitations #
- Subjectivity: The quality and relevance of refinements depend on user interpretation and research context.
- Explainability: While the tool provides textual explanations, the internal decision-making of LLMs is not fully transparent.
- Scope: Currently optimized for English-language research questions and general SLR guidelines.
- Feedback: Feedback is based on the LLM’s’s understanding of Cochrane guidelines and general SLR principles and may not fully account for specific domain nuances.
Planned Improvements #
- Enhanced Guidelines: Extend support for additional domain-specific SLR criteria.
- Explainability Features: Introduce user-facing justifications for each refinement to improve transparency.
- User Feedback Loop: Incorporate mechanisms to learn from user interactions and tailor suggestions more effectively.
Contact Information #
For questions, feedback, or support, please contact support@nested-knowledge.com.
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PALISADE Compliance #
Purpose
The Research Question Refiner helps users align their research questions with systematic review best practices and creates a more precise protocol, ensuring clarity and focus for subsequent workflows.
Appropriateness
The tool is appropriate for its intended application, it directs users towards a more precise research question, by identifying and suggesting population characteristics & data elements pertaining to the review. This corresponds to generally accepted practice in the SLR space when building a study protocol. Furthermore, the LLM models make use of the cochrane guidebook in their prompting, to ensure high quality research questions.
Limitations
The quality and relevance of refinements depend on user interpretation and research context.
- While the tool provides textual explanations, the internal decision-making of LLMs is not fully transparent.
- Feedback is based on the LLM’s understanding of Cochrane guidelines and general SLR principles and may not fully account for specific domain nuances.
- Limitations of the data: Restricted to English-language abstracts; performance may degrade for ambiguous or poorly structured abstracts.
Implementation
The tool uses OpenAI LLMs to evaluate research questions and iteratively refine them through user interaction. This is computationally intensive and requires web access.
Sensitivity and Specificity
Not applicable; the tool does not perform classification.
Algorithm Characteristics
- Design: Single-pass evaluation of quality and iterative refinement via LLM.
Data Characteristics
- Guidelines Used: Based on Cochrane recommendations for systematic literature reviews.
Explainability
The tool provides actionable textual explanations, suggestions, and feedback, making its guidance accessible and understandable to users. Although how such feedback is created is opaque due to the black box nature of LLMs.
Additional Notes on Compliance #
The Research Question Refiner securely shares necessary data with OpenAI to process requests. However, OpenAI does not store this data, and it is not used to train OpenAI’s models, as outlined in our Data Processing Addendum.
