deletions | additions       diff --git a/textbf_Results_textbf_Literature_search__.tex b/textbf_Results_textbf_Literature_search__.tex  index d8ca726..59b0b27 100644  --- a/textbf_Results_textbf_Literature_search__.tex  +++ b/textbf_Results_textbf_Literature_search__.tex 
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\textbf{Results}  \textbf{Literature search }  The Pubmed search resulted in 337 articles from four journals. AfterCovidence  screening for systematic reviews and/or meta-analyses titles and abstracts via Covidence,  79 were exclude search. Coder consensus excluded excluded. Full-text article screening resulted in removal of an additional  74 articles which did not  met the definition of a  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). meta-analysis.  Additionally 2 studies could not be retrieved. In total 182 manuscripts were analyzed for heterogeneity (Figure 1). \textbf{Stata 13.1 Heterogeneity assessment}  Fifty percent (91/182) of all manuscripts analyzed utilized some form or combination of quantitative meta-analyses used at least one  heterogeneity test (Figure 2). A The most widely reported statistic was I2 (9.63\%; 21/218) followed by X2 (8.26\%; 18/218). In combination, X2 and I2 (12.84\%; 28/218) were reported with greatest frequency followed by Q and I2 (12.39\%; 27/218). Authors selected a  random-effects model more frequently than  was the most common meta-analysis used 23\%(42), next came both fixed and random-effect models 18\%(33), fixed-effects was used in 7\%(13) of the manuscripts, and lastly a mixed-effects model was used in 0.92\%(2) of the available manuscripts (Figure 3). This leaves roughly 50\%(91) of available manuscripts with no meta-analysis model in use to quantitatively determine heterogeneity (Figure 3). Of the 42\%(76) which used the random-effects model, 23\%(18) used that model without performing a heterogeneity test to confirm the need for a random-effects model. 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\% p-value with a 61\%(111) occurrence and 0.1\%, 1\%, 20\% as the least frequently used with 2\%(4), 4\%(8), and 2\%(4) occurrences respectively. A forest plot was the most used heterogeneity plot with 40\%(73) while 1\%(2) used a forest and a L’Abbe heterogeneity plot. Out of the manuscripts which created a heterogeneity plot 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), Sensitivity analysis was second 18\%(33), and Meta-regression was used the least 8\%(15) (Figure 4). It was found that 19\%() of the available manuscripts wrote about heterogeneity, but never actually calculated it. 57\%(104) of manuscripts did not find significant heterogeneity, 2\%(4) found enough evidence of heterogeneity to disregard “some” of the meta-analysis, 4\%(7) found significant heterogeneity, and 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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