系统列表化结果研究矩阵（STORM）：一种生成研究假设的方法。
Systematically Tabulated Outcomes Research Matrix (STORM): a methodology to generate research hypotheses.
作者信息
Crompton Joseph G, Oyetunji Tolulope A, Haut Elliott R, Cornwell Edward E, Haider Adil H
机构信息
Department of Surgery, University of California-Los Angeles, Los Angeles, CA.
Department of Surgery, Howard University College of Medicine, Washington, DC.
出版信息
Surgery. 2014 Mar;155(3):541-4. doi: 10.1016/j.surg.2013.10.018. Epub 2013 Oct 14.
BACKGROUND
Here we describe the Systematically Tabulated Outcomes Research Matrix (STORM) method to generate research questions from pre-existing databases with the aim of improving patient outcomes.
MATERIALS AND METHODS
STORM can be applied to a database by tabulating its variables into a matrix of independent variables (y-axis) and dependent variables (x-axis) and then applying each unique pairing of an independent and dependent variable to a patient population to generate potentially meaningful research questions.
RESULTS
To demonstrate this methodology and establish proof-of-principle, STORM was applied on a small scale to the National Trauma Data Bank and generated at least seven clinically meaningful research questions.
CONCLUSION
When coupled with rigorous clinical judgment, the STORM approach complements the traditional method of hypothesis formation and can be generalized to outcomes research using registry databases across different medical specialties.
背景
在此，我们描述了系统列表化结局研究矩阵（STORM）方法，旨在从现有数据库中生成研究问题，以改善患者结局。
材料与方法
STORM可应用于数据库，方法是将其变量列表化为一个自变量（纵轴）和因变量（横轴）的矩阵，然后将自变量和因变量的每一种独特配对应用于患者群体，以生成潜在有意义的研究问题。
结果
为了证明这种方法并建立原理验证，STORM在国家创伤数据库上进行了小规模应用，并生成了至少七个具有临床意义的研究问题。
结论
当与严格的临床判断相结合时，STORM方法补充了传统的假设形成方法，并且可以推广到使用不同医学专业登记数据库的结局研究中。
Zotero 插件