## Appendix C LLM Implementation Details
### Full-Text Section Mapping  
**Functionality Overview:**  
* This function is only used if the article's full text is available directly in PubMed. * Captures only the most relevant sections from an article's full text to be cognizant of token size and context windows. * Does a case-sensitive check to see which of the section titles provided within a given article best matches the required section titles. **Input Variables:**  
* list_of_strings (list): A list of all of an article's section titles to search through. * required_titles (list): A list of titles that are deemed to be the most relevant and helpful to include. **Output Variables:**  
* sections_to_pull (list): A list of matched section titles. **LLM Settings:**  
* gpt-3.5-turbo
* Prompt Strategy: zero-shot learning
* temperature = 0.1  
**Prompt:**  
* Of the given list of sections within the research paper, choose which sections most closely map to an \"Abstract\", \"Background\", \"Methods\",\"Results\", \"Discussion\", \"Conclusion\", \"Sources of Funding\", \"Conflicts of Interest\", \"References\", and \"Table\" section. Only use section names provide in the list to map. Multiple sections can map to each category. If there are multiple sections, separate them using the character!. Format must follow: Abstract: <sections> Background: <sections> Methods: <sections> Results: <sections> Discussion: <sections> Conclusion: <sections> Sources of Funding: <sections> Conflicts of Interest: <sections> Table: <sections> References: <sections>  
_Appendix C.5. Strength of Claim Assessment_  
**Functionality Overview:**  
* Handles multiple article types and sources
* Performs in-depth content analysis and summarization
* Extracts critical metadata and bibliographic information
* Adapts analysis based on article type (review vs. study)
* Focuses on technical details and statistical metrics  
**Input Variables:**  
* article (dict): A dictionary containing the raw article data. **Output Variables:**  
* article_json (dict): A dictionary containing processed article information, including title, publication type, URL, abstract, \"is relevant\" flag, citation, PMID, PMCID, \"full text\" flag, and strength of claim analysis. **LLM Settings:**  
* gpt-4-turbo
* Prompt Strategy: zero-shot learning
* temperature = 0.6
* top_p = 1  
**Prompt for Review Type Articles:**  
* Given the following literature review paper, extract the following information and summarize it, being technical, detailed, and specific, while also explaining concepts for a layman audience. Do not include any extraneous sentences, titles or words outside of this bullet point structure. As often as possible, directly include metrics and numbers (especially significance level, confidence intervals, t-test scores, effect size). Follow the instructions in the parentheses:  
* Purpose (What is the review seeking to address or answer? What methods were used? If relevant and mentioned, include dosages. ):
* Main Conclusions (What are the conclusions and main claims made? What are its implications? ):
* Risks (Are there any risks mentioned (e.g., addiction, death)? ):
* Benefits (Are there any benefits purported? ):
* Search Methodology and Scope (What was the search strategy used to identify relevant literature? Assess the breadth and depth of the literature included. Is the scope clearly defined, and does it encompass relevant research in the field? ):
* Selection Criteria (Evaluate the criteria used for selecting the studies included in the review. What types of studies were included and which were excluded? Were diverse perspectives incorporated? Are contradictory findings or alternative theories addressed?):7. Quality Assessment of Included Studies (Were quality assessment methods applied? How were the methodologies, results, and reliability of the studies assessed? ):  8. Synthesis & Analysis (Evaluate how the findings from different studies are synthesized and analyzed. Is there a clear structure and methodology for synthesizing the literature? What statistical tests were used and for what purpose?