Scholarlink

<p>Large language models (LLMs) have the potential to enhance evidence synthesis efficiency and accuracy. This study assessed LLM-only and LLM-assisted methods in data extraction and risk of bias assessment for 107 trials on complementary medicine. Moonshot-v1-128k and Claude-3.5-sonnet achieved high accuracy (≥95%), with LLM-assisted methods performing better (≥97%). LLM-assisted methods significantly reduced processing time (14.7 and 5.9 min vs. 86.9 and 10.4 min for conventional methods). These findings highlight LLMs’ potential when integrated with human expertise.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Language models for data extraction and risk of bias assessment in complementary medicine

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月31日

查看全文
  • Honghao Lai,
  • Jiayi Liu,
  • Chunyang Bai,
  • Hui Liu,
  • Bei Pan,
  • Xufei Luo,
  • Liangying Hou,
  • Weilong Zhao,
  • Danni Xia,
  • Jinhui Tian,
  • Yaolong Chen,
  • Lu Zhang,
  • Janne Estill,
  • Jie Liu,
  • Xing Liao,
  • Nannan Shi,
  • Xin Sun,
  • Hongcai Shang,
  • Zhaoxiang Bian,
  • Kehu Yang,
  • Luqi Huang,
  • Long Ge,
  • Haodong Li,
  • Ye Wang,
  • Huayu Zhang,
  • Di Zhu,
  • Dongrui Peng,
  • Fan Wang,
  • Yueyan Li,
  • Shilin Tang,
  • Hanxiang Liu,
  • Zeming Li,
  • Zhenhua Yang,
  • Xuan Yu,
  • Yishan Qin

摘要

Large language models (LLMs) have the potential to enhance evidence synthesis efficiency and accuracy. This study assessed LLM-only and LLM-assisted methods in data extraction and risk of bias assessment for 107 trials on complementary medicine. Moonshot-v1-128k and Claude-3.5-sonnet achieved high accuracy (≥95%), with LLM-assisted methods performing better (≥97%). LLM-assisted methods significantly reduced processing time (14.7 and 5.9 min vs. 86.9 and 10.4 min for conventional methods). These findings highlight LLMs’ potential when integrated with human expertise.