Obtaining informed consent (IC) is an ethical imperative, signifying participants’ understanding of the conditions and implications of research participation. One setting where the stakes for understanding are high is randomized controlled trials (RCTs), which test the effectiveness and safety of medical interventions. However, the use of legalese and medicalese in ethical forms coupled with the need to explain RCT-related concepts (e.g. randomization) can increase patients’ cognitive load when reading text. There is a need to systematically examine the language demands of IC documents, including whether the processes intended to safeguard patients by providing clear information might do the opposite through complex, inaccessible language. Therefore, the goal of this study is to build an open-access corpus of patient information sheets (PIS) and consent forms (CF) and analyze each genre using an interdisciplinary approach to capture multidimensional measures of language quality beyond traditional readability measures. A search of publicly-available online IC documents for UK-based cancer RCTs (2000-17) yielded corpora of 27 PIS and 23 CF. Textual analysis using the computational tool, Coh-Metrix, revealed different linguistic dimensions relating to the complexity of IC documents, particularly low word concreteness for PIS and low referential and deep cohesion for CF, although both had high narrativity. Key part-of-speech analyses using Wmatrix corpus software revealed a contrast between the overrepresentation of the pronoun ‘you’ plus modal verbs in PIS and ‘I’ in CF, exposing the contradiction inherent in conveying uncertainty to patients using tentative language in PIS while making them affirm certainty in their understanding in CF.

获得知情同意 (IC) 是一项道德要求，意味着参与者了解参与研究的条件和影响。随机对照试验（RCT）是理解的风险很高的一种情况，它测试医疗干预措施的有效性和安全性。然而，以道德形式使用法律术语和医学术语，加上需要解释随机对照试验相关概念（例如随机化），可能会增加患者在阅读文本时的认知负荷。有必要系统地检查 IC 文件的语言要求，包括旨在通过提供清晰信息来保护患者的流程是否可能通过复杂、难以理解的语言产生相反的效果。因此，本研究的目标是建立一个开放获取的患者信息表（PIS）和同意书（CF）语料库，并使用跨学科方法分析每种类型，以捕获超越传统可读性测量的多维语言质量测量。对基于英国的癌症随机对照试验 (2000-17) 的公开在线 IC 文档的搜索产生了 27 个 PIS 和 23 个 CF 的语料库。使用计算工具 Coh-Metrix 进行的文本分析揭示了与 IC 文档的复杂性相关的不同语言维度，特别是 PIS 的低单词具体性和 CF 的低指称性和深度内聚性，尽管两者都具有较高的叙述性。使用 Wmatrix 语料库软件进行的关键词性分析揭示了 PIS 中代词“you”加情态动词与 CF 中“I”的过度呈现之间的对比，暴露了 PIS 中使用试探性语言向患者传达不确定性所固有的矛盾，而使他们确信自己对 CF 的理解是确定的。