Abstract
BackgroundThis 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.MethodsA 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. s 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.ResultsDecision 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.ConclusionThe 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.
Highlights
This paper proposes the use of decision trees as the basis for automatically extracting information from published randomized controlled trial (RCT) reports
RCTs have a crucial place in the development of the clinical evidence base, and for are the gold-standard in research design for providing evidence of treatment effectiveness
To assess the feasibility of decision trees as a semantic structure or meaning representation for machine extraction, we examine the quality of RCT reporting, the identifiability of RCTs from abstracts, and the completeness and complexity of RCT abstracts
Summary
This paper proposes the use of decision trees as the basis for automatically extracting information from published randomized controlled trial (RCT) reports. The primary evidence for the efficacy of treatments is often documented in reports of randomized controlled trials (RCTs). RCTs have a crucial place in the development of the clinical evidence base, and for are the gold-standard in research design for providing evidence of treatment effectiveness. While much research has focused on developing technologies to assist clinicians to search for evidence [3,4,5,6,7,8], there has been little attention paid to the more challenging textprocessing tasks of evidence extraction and summarization. As the biomedical literature continues to grow, search technologies will probably need to be augmented with such capabilities, to help identify key points in the documents they retrieve, and summarize their meaning for busy clinicians
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