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that all of the coding process was reviewed once over. The papers were analyzed for nine specific  pieces of information: outcome, measurement device, method of  aggregation, primacy of outcome, whether the outcome was a harm or side effect  of an intervention, study design, study type, metric, & sample size. With regards to metric, when an outcome element was implicitly specified, we considered it specified. E.g., For example,  because quantifying survival is, by definition, measuring time to event, specific metric for survival analysis outcomes was always coded as time to event. For survival, remission, and relapse, measurement device was coded as “N/A” because, other than a calendar, there is no measurement device. For outcomes reported using scales (e.g., NCI-CTC), metric was coded as "value at a time point" unless otherwise specified within the body of the article. When coding sample size for studies including non-pediatric-ALL research, all study participants were counted, including adult patients and pediatric patients with a cancer other than ALL.

Analysis:

After ALL.

Analysis:
After  coding, outcomes were grouped into eight domains for analysis: 1) Survival; 2) Mortality; 3) Remission; 4) Relapse; 5) Response to Treatment; 6) Adverse Event; 7) Cognitive  Event; 8) Other.

 Other. 

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 each outcome co-occurred with each specific outcome were recorded in a spreadsheet. Reviewers CW and WB produced the network structure with a symmetrically duplicated matrix, ultimately serving to verify the correct outcomes were recorded to co-occur with the correct corresponding outcomes after the addition of each study. 
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.