Trends of Authors' Conflicts of Interest in Clinical Trials Published in the Journal of Clinical Oncology: A Large Language Model-Assisted Longitudinal Study.

IF 4.6 3区 医学 Q1 ONCOLOGY
Jiasheng Wang, Pedro C Silberman, Sayan Mullick Chowdhury, Bradley W Blaser
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引用次数: 0

Abstract

Purpose: Conflicts of interest (COIs) between clinical trial investigators and biopharmaceutical companies have raised concerns about potential bias in research. This study aimed to systematically analyze the prevalence and trends of COIs in oncology clinical trials published in the Journal of Clinical Oncology (JCO) for the past 15 years and to demonstrate the utility of large language models (LLMs) for automated data extraction in this context.

Methods: We identified clinical trials published in the JCO from 2010 to 2025 using PubMed. We extracted publication data and author disclosures from the JCO Web site. OpenAI's GPT-4o was used to identify the main medical product studied and the related biopharmaceutical company and their variants in author disclosures. We then analyzed COI trends across three time periods (2010-2015, 2015-2020, 2020-2025).

Results: GPT-4o demonstrated close to 95% accuracy in identifying medical products and companies. Of the 2,583 clinical trials, 2,219 (85.9%) involved a medical product. Among these, 1,610 (72.6%) had at least one author with a COI related to the associated biopharmaceutical company. COI prevalence increased from 70.0% (2010-2015) to 77.0% (2015-2020), and then decreased to 72.0% (2020-2025). Company employment, advisory roles, and honoraria were common COI types and followed similar trends. US-led studies had a significantly higher COI prevalence than those from other regions (77.6% v 67.3%; P < .001). Additionally, 61.9% of first or last authors had a COI, which increased consistently over three time periods.

Conclusion: This study reveals widespread COIs in oncology clinical trials, particularly in US-led studies and among leading authors, with discernible temporal patterns. The LLM-based method provides an efficient solution for COI monitoring, promoting transparency in biomedical research.

《临床肿瘤学杂志》发表的临床试验中作者利益冲突的趋势:一项大型语言模型辅助纵向研究。
目的:临床试验研究者和生物制药公司之间的利益冲突(COIs)引起了人们对研究中潜在偏见的担忧。本研究旨在系统分析过去15年来发表在《临床肿瘤学杂志》(JCO)上的肿瘤临床试验中coi的患病率和趋势,并证明在这种情况下,大型语言模型(llm)在自动数据提取方面的实用性。方法:我们使用PubMed检索2010年至2025年在JCO上发表的临床试验。我们从JCO网站提取出版数据和作者信息。OpenAI的gpt - 40用于识别研究的主要医疗产品以及作者披露的相关生物制药公司及其变体。然后,我们分析了三个时间段(2010-2015年、2015-2020年、2020-2025年)的COI趋势。结果:gpt - 40在识别医疗产品和公司方面的准确率接近95%。在2,583项临床试验中,2,219项(85.9%)涉及医疗产品。其中,1610篇(72.6%)至少有一名作者的COI与相关生物制药公司相关。COI患病率从70.0%(2010-2015年)上升至77.0%(2015-2020年),然后下降至72.0%(2020-2025年)。公司雇佣、顾问角色和酬金是常见的COI类型,并遵循类似的趋势。美国主导的研究的COI患病率明显高于其他地区(77.6% vs 67.3%;P < 0.001)。此外,61.9%的第一或最后作者具有COI,在三个时间段内持续增加。结论:本研究揭示了肿瘤临床试验中广泛存在的coi,特别是在美国主导的研究和主要作者中,具有可识别的时间模式。基于llm的方法为COI监测提供了有效的解决方案,提高了生物医学研究的透明度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.40
自引率
7.50%
发文量
518
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