PubMed · 12518588
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Stephen Price. 2002. Trial and error.. https://doi.org/10.7748/nm2002.12.9.8.8.c2137
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STUDY OBJECTIVE: We characterize the quantity and quality of data tables and figures in reports of randomized controlled trials (RCTs) submitted to the British Medical Journal (BMJ) and published in peer-reviewed journals. We investigate how the peer review process affected table and figure quality. METHODS: We reviewed 62 consecutive reports of RCTs submitted to the BMJ in 2001 that were later published in the BMJ (n=12) or elsewhere. We counted and categorized the tables and figures in both the initial submissions and published articles. Using standardized instruments and procedures, we analyzed the quality of these tables and figures and checked BMJ editorial documents to see whether changes were triggered by their review process. RESULTS: The numbers of tables and figures did not change markedly between submission and publication. Five percent of publications had no data tables; 56% had no data figures. Data density was low for published tables and figures. Tables seldom showed data stratified on important covariates; 88% of published tables were simple lists or were stratified on only 1 variable. Less than half the figures met their data presentation potential, with most failing to portray by-subject data and few displaying advanced features such as pairing, symbolic dimensionality, or small multiples. BMJ external peer reviewers seldom commented on tables or figures. CONCLUSION: Tables and figures can convey details and complex relationships not easily described in text. Although tables are included in most submitted and published articles, they are not presented optimally; figures are used sparingly and are also of suboptimal quality. Journals should consider improving their table and figure quality in the hope that improved graphics will empower readers to scrutinize the data, thereby dissuading authors from presenting biased analyses and misrepresented conclusions.
BACKGROUND AND PURPOSE: Radiation treatment (RT) for cancer is susceptible to clinical incidents resulting from human errors and equipment failures. A systematic approach to collecting and processing incidents is required to manage patient risks. We describe the application of a new taxonomic structure for RT that supports risk analysis and organizational learning. MATERIALS AND METHODS: A systematic analysis of the RT process identified five process domains. Within each domain we defined incident type groups. We then constructed a database reflecting this taxonomic structure and populated it with incidents from publicly available sources. Querying this database provides insights into the nature and relative frequency of incidents in RT. RESULTS: There are relatively few reports of incidents in the Prescription domain compared with the Preparation and Treatment domains. There are also fewer reports of systematic and infrastructure incidents in comparison to sporadic and process incidents. Infrastructure incidents are mainly systematic in nature, while process incidents are more likely to be sporadic. CONCLUSIONS: The lack of a standard, systems-oriented framework for incident reporting makes it difficult to learn from existing incident report sources. A clear understanding of the potential consequences and relationships between different incident types will guide incident reporting, resource allocation, and risk management efforts.
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