研究信:用大型语言模型描述精神病临床研究文献中的旋转
Roy H Perlis1,2
1Center for Quantitative Health, Massachusetts General Hospital, Boston, MA.
medRxiv : the preprint server for health sciences
|July 15, 2024
概括
近一半的精神科干预摘要显示有偏见的报道,称为旋转. 大型语言模型可以帮助检测这种旋转,以提高出版物的科学准确性.
科学领域:
- 精神病学是一个精神病学.
- 医学出版 医学出版
- 人工智能在医学中的应用
背景情况:
- 旋转,或有偏见的报道,错误地将研究结果描述为比合理的更积极.
- 在精神病学文献中,旋转的流行程度尚不清楚.
研究的目的:
- 使用大型语言模型 (LLM) 来评估精神病学干预摘要中的旋转程度.
- 在药理和非药理干预中描述旋转.
主要方法:
- 分析了来自3个具有影响力的精神病学期刊 (2013-2023) 的663个摘要,报告了随机试验或元分析.
- 使用GPT-4 Turbo (gpt-4-turbo-2024-04-09) 检测旋转,根据专家注释的摘要进行验证.
主要成果:
- 44.6%的摘要表现出旋转:43.5%的随机试验和49.3%的元分析.
- 脊柱在47.7%的药物,45.0%的心理治疗和35.7%的其他干预摘要中存在.
- 在随机试验报告,非药物/非心理治疗干预和最近的出版物中,spin不太常见.
结论:
- 很大一部分精神科干预摘要可能会误导性地呈现结果,可能会影响临床实践.
- 使用LLM的自动旋转检测显示出在科学出版物中识别和减少旋转的前景.
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