deletions | additions       diff --git a/textbf_Results_textbf_Literature_search__.tex b/textbf_Results_textbf_Literature_search__.tex  index df7d368..b1d64b4 100644  --- a/textbf_Results_textbf_Literature_search__.tex  +++ b/textbf_Results_textbf_Literature_search__.tex 
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The Pubmed search resulted in 337 articles from four journals. After Covidence screening for systematic reviews and/or meta-analyses 79 were exclude search. Coder consensus excluded 76 articles which met the definition of systematic review and/or meta-analysis, yet were better categorized into other study types (Individual patient data study, human trials, Histologic studies, Genetic studies, letters to the editor, and Genomic microarray meta analyses). Additionally 2 studies could not be retrieved. In total 182 manuscripts were analyzed for heterogeneity (Figure 1).   \textbf{Stata 13.1 Heterogeneity assessment}  Heterogeneity was utilized in 50%(91) 50\%(91)  of all manuscripts analyzed utilized some form or combination of quantitative heterogeneity test (Figure 2). A random-effects model was the most common meta-analysis used 23%(42), 23\%(42),  next came both fixed and random-effect models 18%(33), 18\%(33),  fixed-effects was used in 7%(13) 7\%(13)  of the manuscripts, and lastly a mixed-effects model was used in 0.92%(2) 0.92\%(2)  of the available manuscripts (Figure 3). This leaves roughly 50%(91) 50\%(91)  of available manuscripts with no meta-analysis model in use to quantitatively determine heterogeneity (Figure 3). Of the 42%(76) 42\%(76)  which used the random-effects model, 23%(18) 23\%(18)  used that model without performing a heterogeneity test to confirm the need for a random-effects model. 12%(22) 12\%(22)  of the manuscripts changed from the fixed-effects meta-analysis model to the random-effects after a heterogeneity test. The significance level most used was a 5% 5\%  p-value with a 61%(111) 61\%(111)  occurrence and 0.1%, 1%, 20% 0.1\%, 1\%, 20\%  as the least frequently used with 2%(4), 4%(8), 2\%(4), 4\%(8),  and 2%(4) 2\%(4)  occurrences respectively. A forest plot was the most used heterogeneity plot with 40%(73) 40\%(73)  while 1%(2) 1\%(2)  used a forest and a L’Abbe heterogeneity plot. Out of the manuscripts which created a heterogeneity plot 41%(37) 41\%(37)  actually presented them. Of the three tests designed to investigate heterogeneity (Subgroup, Meta-regression, and Sensitivity Analyses), Subgroup analysis was used the most 22%(40), 22\%(40),  Sensitivity analysis was second 18%(33), 18\%(33),  and Meta-regression was used the least 8%(15) 8\%(15)  (Figure 4). It was found that 19%() 19\%()  of the available manuscripts wrote about heterogeneity, but never actually calculated it. 57%(104) 57\%(104)  of manuscripts did not find significant heterogeneity, 2%(4) 2\%(4)  found enough evidence of heterogeneity to disregard “some” of the meta-analysis, 4%(7) 4\%(7)  found significant heterogeneity, and 37%(67) 37\%(67)  did not know whether or not there was too much heterogeneity present to perform a meta-analysis.Summarization of evidence mapping efforts \textbf{First Objective}  Table 1 from the evidence mapping method from (Althius 2014) was applied to a systematic review, Maltoni et al. This qualitative method of me . (not done with this one yet, i'm figuring out how to format it in excell) 
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