在没有全文的情况下预测实质性的生物医学引用
Travis A Hoppe1, Salsabil Arabi2, B Ian Hutchins2
1Office of the Director, National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD 20782.
概括
生物医学引用分析可以识别研究加速. 一个新的模型将实质知识转移与同行评审的补充区分开来,揭示了美国联邦政府资助的研究在关键的基础到临床知识流中占有三倍的比例.
科学领域:
- 生物医学信息学是生物医学信息学.
- 图书统计学 图书统计学
- 研究影响分析研究影响分析.
背景情况:
- 生物医学引用网络为研究加速和翻译提供了洞察力.
- 当前引用分析通常假设所有引用都代表实质性的知识转移.
- 引用可能是由于直接知识贡献以外的其他原因而被添加的,特别是在同行评审期间.
研究的目的:
- 开发一个预测模型来识别晚期引用,通常在同行评审期间添加.
- 要区分实质知识转移和其他引用类型.
- 量化美国联邦政府资助的研究对早期知识转移的贡献.
主要方法:
- 利用了开放访问数据的全面功能集.
- 训练了一个预测模型,只使用文章标题,摘要和之前的引用.
- 该模型避免了全文分析和未来的引用模式,以便在早期适用.
主要成果:
- 该模型成功地识别了后期引用,包括在审查期间添加的修辞性引用,如期刊自我引用.
- 较低的预测分数准确地标记了早期的引用,表明实质性的知识转移.
- 美国联邦政府资助的研究占从基础到临床研究预测的实质知识转移的30%,尽管仅占文献的10%.
结论:
- 开发的模型有效地区分了实质知识转移与同行评审影响的引用.
- 美国联邦政府资助的生物医学研究显示,在推动早期转化知识流动方面存在明显的过度代表性.
- 这一发现凸显了美国联邦资助对推动生物医学研究从基础科学到临床应用产生不成比例的影响.
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