Computational Methods, Resources, and Tools to Assess Transparency and Rigor of Randomized Clinical Trials

项目概要/摘要 随机对照试验（RCT）是循证医学的基石，在临床研究中占有重要地位。 “证据金字塔”当严格设计、执行和报告时，它们提供了最有力的证据， 治疗干预的有效性。然而，他们通常遭受各种类型的偏见（例如， 选择偏倚、损耗偏倚）。在报告方面，关键的方法学特征， 由于随机化和盲法往往被忽略，使得难以评估试验的有效性和适用性 调查结果。遵守报告准则可以提高报告的透明度和完整性， 生物医学研究SPIRIT和CONSORT指南帮助作者报告RCT方案和结果 出版物，分别。虽然得到了许多高影响力医学期刊的认可，但坚持这些 指南仍然不是最佳的，可能是因为期刊缺乏执行和验证的方法， 涉及大量的期刊工作人员或编辑时间。此外，透明的报告不 保证方法的严谨性。我们假设自然语言处理（NLP）方法支持 通过SPIRIT/CONSORT指南以及用于临床研究的术语和本体资源， (a)通过在RCT报告中定位关键研究特征并标记其缺失来提高依从性，以及（B） 支持自动化的严谨性评估和大规模的方法研究，通过提取颗粒机器- 来自RCT报告的可读方法学信息。为了实现这些目标，我们的具体目标是： 目标1。创建文本分类模型，以评估RCT报告的透明度和完整性 与SPIRIT和CONSORT指南。 目标二。开发信息提取方法，以识别RCT报告中的方法学特征。 目标3。建立一个基于网络的合规工具，生成关于透明度和准则遵守情况的报告， RCT报告。 目标4。从已发表的RCT文献中生成结构化透明度报告，用于方法学分析， 报告质量。 拟议的研究将开发一套模型，资源和工具，以帮助临床利益相关者 在保持高报告标准、综合证据和促进开放科学实践方面进行研究。 它们将有助于改善整个科学生态系统，从而改善临床护理， 卫生政策。

Project Summary/Abstract Randomized controlled trials (RCTs) are a cornerstone of evidence-based medicine and are placed high in the “evidence pyramid”. When rigorously designed, conducted, and reported, they provide the most robust evidence on effectiveness of therapeutic interventions. However, they commonly suffer from various types of biases (e.g., selection bias, attrition bias) in study design and execution. In reporting, key methodological characteristics such as randomization and blinding are often omitted, making it difficult to assess the validity and applicability of trial findings. Adherence to reporting guidelines can improve transparency and completeness of reporting for biomedical studies. SPIRIT and CONSORT guidelines help authors report RCT protocols and results publications, respectively. Although endorsed by many high-impact medical journals, adherence to these guidelines remains suboptimal, possibly because journals lack methods for enforcement and verification, which involves a substantial amount of journal staff or editorial time. Furthermore, transparent reporting does not guarantee methodological rigor. We hypothesize that natural language processing (NLP) methods underpinned by SPIRIT/CONSORT guidelines as well as terminological and ontological resources for clinical research can (a) improve compliance by locating key study characteristics in RCT reports and flagging their absence, and (b) support automated rigor assessment and large-scale methodological research by extracting granular machine- readable methodological information from RCT reports. To achieve these goals, we specifically aim to: Aim 1. Create text classification models for assessing transparency and completeness of RCT reports consistent with SPIRIT and CONSORT guidelines. Aim 2. Develop information extraction methods to identify methodological characteristics in RCT reports. Aim 3. Build a web-based compliance tool that generates reports on transparency and guideline adherence of RCT reports. Aim 4. Generate structured transparency reports from published RCT literature for analysis of methodology and reporting quality. The proposed research will develop a set of models, resources, and tools that will assist stakeholders of clinical research in maintaining high reporting standards, synthesizing evidence, and promoting open science practices. They will contribute to improvements throughout the scientific ecosystem, leading to better clinical care and health policy.