Ann Intern Med. 2015 Feb 17;162(4):287-94. doi: 10.7326/M14-1603.
Data abstraction is a key step in conducting systematic reviews because data collected from study reports form the basis of appropriate conclusions. Recent methodological standards and expectations highlight several principles for data collection. To support implementation of these standards, this article provides a step-by-step tutorial for selecting data collection tools; constructing data collection forms; and abstracting, managing, and archiving data for systematic reviews. Examples are drawn from recent experience using the Systematic Review Data Repository for data collection and management. If it is done well, data collection for systematic reviews only needs to be done by 1 team and placed into a publicly accessible database for future use. Technological innovations, such as the Systematic Review Data Repository, will contribute to finding trustworthy answers for many health and health care questions.
数据抽象是进行系统评价的关键步骤，因为从研究报告中收集的数据是得出恰当结论的基础。最近的方法学标准和期望强调了数据收集的几个原则。为了支持这些标准的实施，本文提供了一个逐步的教程，用于选择数据收集工具；构建数据收集表；以及为系统评价提取、管理和存档数据。示例取自最近使用系统评价数据存储库进行数据收集和管理的经验。如果做得好，系统评价的数据收集只需由一个团队完成，并将其放入公共可访问的数据库中以备将来使用。技术创新，如系统评价数据存储库，将有助于为许多健康和医疗保健问题找到可信的答案。
