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Journal of evidence-based medicine. 2025 Mar;18(1):e70017. doi: 10.1111/jebm.70017 Q13.62024

From Manual to Machine: Revolutionizing Day Surgery Guideline and Consensus Quality Assessment With Large Language Models

从人工到机器:使用大型语言模型革新日间手术指南和共识的质量评估 翻译改进

Xingyu Wan  1, Ruiyan Wang  1, Junxian Zhao  2, Tianhu Liang  2, Bingyi Wang  3, Jie Zhang  3, Yujia Liu  1, Yan Ma  1, Yaolong Chen  2  3  4, Xinghua Lv  1  5

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作者单位

  • 1 The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • 2 Research Center for Clinical Medicine, the First Hospital of Lanzhou University, Lanzhou, China.
  • 3 School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
  • 4 Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
  • 5 Day Surgery Center, the First Hospital of Lanzhou University, Lanzhou, China.
  • DOI: 10.1111/jebm.70017 PMID: 40123109

    摘要 Ai翻译

    Objective: To evaluate the methodological and reporting quality of clinical practice guidelines/expert consensus for ambulatory surgery centers published since 2000, combining manual assessment with large language model (LLM) analysis, while exploring LLMs' feasibility in quality evaluation.

    Methods: We systematically searched Chinese/English databases and guideline repositories. Two researchers independently screened literature and extracted data. Quality assessments were conducted using AGREE II and RIGHT tools through both manual evaluation and GPT-4o modeling.

    Results: 54 eligible documents were included. AGREE II domains showed mean compliance: Scope and purpose 25.00%, Stakeholder involvement 20.16%, Rigor of development 17.28%, Clarity of presentation 41.56%, Applicability 18.06%, Editorial independence 26.39%. RIGHT items averaged: Basic information 44.44%, Background 36.11%, Evidence 14.07%, Recommendations 34.66%, Review and quality assurance 3.70%, Funding and declaration and management of interests 24.54%, Other information 27.16%. LLMs'-evaluated documents demonstrated significantly higher scores than manual assessments in both tools. Subgroup analyses revealed superior quality in documents with evidence retrieval, conflict disclosure, funding support, and LLM integration (P <0.05).

    Conclusion: Current guidelines and consensus related to day surgery need to improve their methodological quality and quality of reporting. The study validates LLMs' supplementary value in quality assessment while emphasizing the necessity of maintaining manual evaluation as the foundation.

    Keywords: AGREE II; LLM; RIGHT; consensus; day surgery; guideline; quality assessment.

    Keywords:Day Surgery Guideline; Quality Assessment; Large Language Models

    Copyright © Journal of evidence-based medicine. 中文内容为AI机器翻译,仅供参考!

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    期刊名:Journal of evidence-based medicine

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    ISSN:1756-5383

    e-ISSN:1756-5391

    IF/分区:3.6/Q1

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    From Manual to Machine: Revolutionizing Day Surgery Guideline and Consensus Quality Assessment With Large Language Models