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\title{PedOnc\\}


\author{C. Cole Wayant}
\affil{Affiliation not available}

  
\author{Timothy Nissen}
\affil{Affiliation not available}
  
\author{Jordan Wiebe}
\affil{Affiliation not available}
  
\author{Jason Beaman}
\affil{Affiliation not available}
  
\author{Matt Vassar}
\affil{Affiliation not available}
  



\date{\today}

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Search Criteria:\\

We searched for clinical trials that investigate acute lymphoblastic
leukemia (ALL) in the pediatric population.~ PubMed was searched to
identify relevant studies. The search strategy is listed in Appendix 1.
~The search was performed on Tuesday, November 10, 2015.\\

Coding:\\

Coding, the process by which the published papers in our sample were
read, reviewed, and documented, was done by C.C.W., W.D.B., J. W., and
T.E.N. Articles used were the result of a PubMed search over the past 10
years. At the onset, 1086 potentially relevant papers were assembled.
Covidence.org was used as a means of screening abstracts and titles to
remove papers that were not useful. Each article required two separate
`yes' votes to be included in our study, and two separate `no' votes to
be excluded. Disputes were resolved via group discussion. 885 articles
were included in our study of which 285 were randomly sampled and
divided evenly four ways. After full text screening, 182 articles were
included. Animal studies, follow-up studies of adult survivors of
pediatric ALL, exclusively genetic studies, and genome-wide association
studies were excluded. Meta-analyses were excluded to avoid redundancy,
as we had already coded their constituent studies individually. Studies
combining original research with meta-analysis were included so as to
not exclude original research. (See Prisma Diagram).\\

An abstraction manual was formulated by D.H. to standardize the coding
process.~Each person was partnered with another so 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. 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. For the final step in 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.\\

Analysis:\\

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 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.\\

Appendix 1:\\
((((acute AND lymphoblast* AND (leukem* OR leukaem*)) OR ``Precursor
Cell Lymphoblastic Leukemia-Lymphoma''{[}Mesh{]}))\\
AND\\
(Infan* OR newborn* OR new-born* OR perinat* OR neonat* OR baby OR baby*
OR babies OR toddler* OR minors OR minors* OR boy OR boys OR boyhood OR
girl* OR kid OR kids OR child* OR children* OR schoolchild* OR
(school{[}ti{]} AND child{[}ti{]}) OR adolescen* OR juvenil* OR youth*
OR teen* OR underage* OR pubescen* OR pediatrics{[}MeSH Terms{]} OR
pediatric* OR paediatric* OR peadiatric* OR prematur* OR preterm*)
Filters: Clinical Trial; Clinical Trial, Phase I; Clinical Trial, Phase
II; Clinical Trial, Phase III; Clinical Trial, Phase IV; Controlled
Clinical Trial; Randomized Controlled Trial; published in the last 10
years.

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