A deep learning platform to evaluate the reliability of scientific claims by citation analysis.

阿片类药物在美国的流行可以追溯到1980年的一封信，信中报道了 享有盛誉的《新英格兰医学杂志》指出，合成阿片类药物不会上瘾。姗姗来迟的 引文分析导致《华尔街日报》在这封信的末尾附加了一条警告，这封信已经被 并被不加批判地引用为“阿片类药物治疗成瘾罕见的证据”。这场流行病只是 不可靠和未经批判性引用的科学主张如何影响公众健康的一个例子是， 来自行业报告的研究表明，生物医学报告的很大一部分不能 经过独立验证。然而，目前还没有公开可用的资源或指标来确定如何 可靠的科学主张是不成为该学科的专家，也不保留一个。总数 引文计数是常用的衡量标准，但从本质上讲，它并不能很好地反映研究质量 因为证实和驳斥引文是平等的，而期刊的威望 并不能保证在那里发布的声明是真实的。缺乏衡量数据真实性的指标 报道的索赔每年给公众、企业和政府造成数十亿美元的损失。我们 已经开发出一种原型，可以自动将引用科学主张的声明分类为 三类：那些提供支持或反驳证据的，或仅仅提到 认领。这一独特的功能使SCITE用户能够分析科学主张的可靠性，网址为 前所未有的规模和速度，帮助他们做出更明智的决策。这个 Prototype吸引了顶级生物技术和制药行业的潜在客户 公司、研究机构、学术界和学术出版商。我们建议进行 通过优化原型的效率和准确性将SCITE提炼成MVP的研究，直到 它们达到了可行的里程碑，并将完善适合我们滩头市场的产品市场， 学术出版，其对研究的完整性和可靠性的影响很难 高估了。

The opioid epidemic in the United States has been traced to a 1980 letter reporting in the prestigious New England Journal of Medicine that synthetic opioids are not addictive. A belated citation analysis led the journal to append this letter with a warning this letter has been “heavily and uncritically cited” as evidence that addiction is rare with opioid therapy.” This epidemic is but one example of how unreliable and uncritically cited scientific claims can affect public health, as studies from industry report that a substantial part of biomedical reports cannot be independently verified. Yet, there is no publicly available resource or indicator to determine how reliable a scientific claim is without becoming an expert on the subject or retaining one. The total citation count, the commonly used measure, is inherently a poor proxy for research quality because confirming and refuting citations are counted as equal, while the prestige of the journal is not a guarantee that a claim published there is true. The lack of indicators for the veracity of reported claims costs the public, businesses, and governments, billions of dollars per year. We have developed a prototype that automatically classifies statements citing a scientific claim into three classes: those that provide supporting or contradicting evidence, or merely mention the claim. This unique capability enables scite users to analyze the reliability of scientific claims at an unprecedented scale and speed, helping them to make better-informed decisions. The prototype has attracted potential customers among top biotechnology and pharmaceutical companies, research institutions, academia, and academic publishers. We propose to conduct research that will refine scite into an MVP by optimizing prototype efficiency and accuracy until they reach feasible milestones, and will refine the product-market fit in our beachhead market, academic publishing, whose influence on the integrity and reliability of research is difficult to overestimate.