Chung Grace Y, Coiera Enrico
Centre for Health Informatics, University of New South Wales, Sydney, NSW, 2052, Australia.
BMC Med Inform Decis Mak. 2008 Oct 28;8:48. doi: 10.1186/1472-6947-8-48.
This paper proposes the use of decision trees as the basis for automatically extracting information from published randomized controlled trial (RCT) reports. An exploratory analysis of RCT abstracts is undertaken to investigate the feasibility of using decision trees as a semantic structure. Quality-of-paper measures are also examined.
A subset of 455 abstracts (randomly selected from a set of 7620 retrieved from Medline from 1998 - 2006) are examined for the quality of RCT reporting, the identifiability of RCTs from abstracts, and the completeness and complexity of RCT abstracts with respect to key decision tree elements. Abstracts were manually assigned to 6 sub-groups distinguishing whether they were primary RCTs versus other design types. For primary RCT studies, we analyzed and annotated the reporting of intervention comparison, population assignment and outcome values. To measure completeness, the frequencies by which complete intervention, population and outcome information are reported in abstracts were measured. A qualitative examination of the reporting language was conducted.
Decision tree elements are manually identifiable in the majority of primary RCT abstracts. 73.8% of a random subset was primary studies with a single population assigned to two or more interventions. 68% of these primary RCT abstracts were structured. 63% contained pharmaceutical interventions. 84% reported the total number of study subjects. In a subset of 21 abstracts examined, 71% reported numerical outcome values.
The manual identifiability of decision tree elements in the abstract suggests that decision trees could be a suitable construct to guide machine summarisation of RCTs. The presence of decision tree elements could also act as an indicator for RCT report quality in terms of completeness and uniformity.
本文提议将决策树用作从已发表的随机对照试验（RCT）报告中自动提取信息的基础。对RCT摘要进行探索性分析，以研究将决策树用作语义结构的可行性。还对论文质量指标进行了考察。
从1998年至2006年从Medline检索到的7620篇文献中随机抽取455篇摘要，考察RCT报告的质量、从摘要中识别RCT的能力，以及RCT摘要在关键决策树元素方面的完整性和复杂性。摘要被手动分为6个亚组，区分它们是主要的RCT还是其他设计类型。对于主要的RCT研究，我们分析并注释了干预比较、人群分配和结果值的报告情况。为衡量完整性，统计了摘要中完整报告干预、人群和结果信息的频率。对报告语言进行了定性检查。
在大多数主要的RCT摘要中，决策树元素可手动识别。随机抽取的子集中有73.8%是主要研究，其中单一人群被分配接受两种或更多种干预。这些主要的RCT摘要中有68%具有结构。63%包含药物干预。84%报告了研究对象的总数。在抽取的21篇摘要子集中，71%报告了数值结果值。
摘要中决策树元素的手动可识别性表明，决策树可能是指导RCT机器总结的合适结构。决策树元素的存在也可作为RCT报告在完整性和一致性方面质量的一个指标。
