Evidence map›Paper›PMID 40251310›Full record

ArticleSurgical endoscopy2025

Large language model-generated clinical practice guideline for appendicitis.

Amy Boyle, Bright Huo, Patricia Sylla, Elisa Calabrese, Sunjay Kumar, Bethany J Slater, Danielle S Walsh, R Wesley Vosburg

Abstract read
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Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Comparing AI chat tools and PubMed for medical literature searches.Journal of the Medical Library Association : JMLA · 2026
    Article
  3. Article
  4. Article
  5. Large Language Models in Surgery: Promise, Pitfalls, and Practical Use.Journal of abdominal wall surgery : JAWS · 2026
    Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Amy BoyleMichael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Bright HuoDivision of General Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.ORCID http://orcid.org/0000-0003-4999-4328
Patricia SyllaDivision of Colon and Rectal Surgery, Department of Surgery, Mount Sinai Hospital, New York, NY, USA.
Elisa CalabreseDepartment of Surgery, University of Adelaide, The Queen Elizabeth Hospital, Adelaide, SA, Australia.
Sunjay KumarDepartment of General Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA.
Bethany J SlaterDepartment of Surgery, University of Chicago, Chicago, IL, USA.
Danielle S WalshProfessor of Surgery, Department of Surgery, University of Kentucky, Lexington, KY, USA.
R Wesley VosburgDepartment of Surgery, Grand Strand Medical Center, Myrtle Beach, SC, USA. ralph.vosburg@hcahealthcare.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical practice guidelines provide important evidence-based recommendations to optimize patient care, but their development is labor-intensive and time-consuming. Large language models have shown promise in supporting academic writing and the development of systematic reviews, but their ability to assist with guideline development has not been explored. In this study, we tested the capacity of LLMs to support each stage of guideline development, using the latest SAGES guideline on the surgical management of appendicitis as a comparison.

methodsPrompts were engineered to trigger LLMs to perform each task of guideline development, using key questions and PICOs derived from the SAGES guideline. ChatGPT-4, Google Gemini, Consensus, and Perplexity were queried on February 21, 2024. LLM performance was evaluated qualitatively, with narrative descriptions of each task's output. The Appraisal of Guidelines for Research and Evaluation in Surgery (AGREE-S) instrument was used to quantitatively assess the quality of the LLM-derived guideline compared to the existing SAGES guideline.

resultsPopular LLMs were able to generate a search syntax, perform data analysis, and follow the GRADE approach and Evidence-to-Decision framework to produce guideline recommendations. These LLMs were unable to independently perform a systematic literature search or reliably perform screening, data extraction, or risk of bias assessment at the time of testing. AGREE-S appraisal produced a total score of 119 for the LLM-derived guideline and 156 for the SAGES guideline. In 19 of the 24 domains, the two guidelines scored within two points of each other.

conclusionsLLMs demonstrate potential to assist with certain steps of guideline development, which may reduce time and resource burden associated with these tasks. As new models are developed, the role for LLMs in guideline development will continue to evolve. Ongoing research and multidisciplinary collaboration are needed to support the safe and effective integration of LLMs in each step of guideline development.

Indexed as

AppendectomyAppendicitisPractice Guidelines as TopicHumansLanguageLarge Language ModelsAppendicitisChatGPTClinical practice guidelineGenerative AILarge language modelsSurgery

Identifiers

PMID40251310

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.