Descriptive statistics were computed using Stata  software was used to analyze frequency of appearance of unique outcomes and the specification of the nine outcome elements outlined in the abstraction manual. Unique outcomes were then placed in the eight broad domains listed above and run through STATA Stata  to reveal larger trends in reporting.

In order to structure a visual representation and calculate centrality of clinical outcomes in pediatric leukemia, a matrix was constructed. The foundation of this social network was formed using a basis of frequency of connections across outcomes, termed co-occurrences. Each outcome and the number of times it co-occurred with other specific outcomes were recorded in a spreadsheet. Reviewers C.C.W. and W.D.B. produced the network structure with a symmetrically duplicated matrix, ultimately serving to verify the co-occurrences.

We imported the network matrix onto UCINET and used Netdraw software. Each outcome was uploaded onto the program in the order of total co-occurrences. Thus, each outcome was sized in increasingly larger nodes, the plots of FIGURE **; the larger the size of the node, then the larger number of total co-occurrences this outcome maintains across outcomes in pediatric acute lymphoblastic leukemia. Next, the spring embedding function was applied to group outcomes around the largest nodes. This was accomplished by grouping less connected outcomes around nodes in a pattern of descending number of co-occurrences until the network became too dense for coherency. Next, a superstructure was formed, according to FIGURE **, which represents the social network architecture of outcomes.