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Related Concept Videos

Appendicitis-II: Diagnostic Studies and Management01:29

Appendicitis-II: Diagnostic Studies and Management

47
Diagnosing and managing appendicitis requires a structured and comprehensive approach that spans from initial assessment to postoperative care. Here is an overview of the process:
Diagnosing Appendicitis
It requires a multifaceted approach, starting with a detailed physical examination to pinpoint the location and nature of the pain and identify any associated symptoms. Laboratory tests play a crucial role. A complete Blood Count (CBC) typically reveals leukocytosis (an increased number of...
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Appendicitis-I: Introduction01:22

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The appendix, a small, narrow, blind tube extending from the inferior part of the cecum, is widely regarded as a vestigial organ, having lost much of its original function through evolution. Despite its diminished role, the appendix can become inflamed, a condition known as appendicitis.
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Murine Appendectomy Model of Chronic Colitis Associated Colorectal Cancer by Precise Localization of Caecal Patch
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Large language model-generated clinical practice guideline for appendicitis.

Amy Boyle1, Bright Huo2, Patricia Sylla3

  • 1Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.

Surgical Endoscopy
|April 18, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) can aid clinical guideline development by generating search syntax and performing data analysis. However, they currently cannot independently conduct literature searches or perform screening and risk of bias assessments.

Keywords:
AppendicitisChatGPTClinical practice guidelineGenerative AILarge language modelsSurgery

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Practice Guideline Development

Background:

  • Clinical practice guidelines are crucial for evidence-based patient care but are resource-intensive to develop.
  • Large language models (LLMs) show promise in academic writing and systematic reviews, but their role in guideline development is unexplored.
  • This study evaluates LLM capabilities across all stages of guideline development, using a surgical appendicitis guideline as a benchmark.

Purpose of the Study:

  • To assess the capacity of current large language models (LLMs) to support the various stages of clinical practice guideline development.
  • To compare the quality of an LLM-generated guideline with an established guideline using the AGREE-S instrument.

Main Methods:

  • Engineered prompts based on key questions and PICOs from the SAGES appendicitis guideline to guide LLM tasks.
  • Queried ChatGPT-4, Google Gemini, Consensus, and Perplexity on February 21, 2024.
  • Qualitative evaluation of LLM outputs and quantitative assessment using the Appraisal of Guidelines for Research and Evaluation in Surgery (AGREE-S) instrument.

Main Results:

  • LLMs successfully generated search syntax, performed data analysis, and applied the GRADE and Evidence-to-Decision frameworks for recommendations.
  • LLMs were unable to independently perform systematic literature searches, screening, data extraction, or risk of bias assessments.
  • The LLM-derived guideline scored 119/156 on the AGREE-S instrument, with 19 of 24 domains scoring within two points of the SAGES guideline.

Conclusions:

  • LLMs show potential to streamline guideline development, reducing time and resource burdens for specific tasks.
  • The capabilities of LLMs in guideline development are evolving with new model advancements.
  • Multidisciplinary collaboration and further research are essential for the safe and effective integration of LLMs into guideline creation.