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The Pubmed search resulted in 337 articles from four journals. After screening titles and abstracts via Covidence, 79 were 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. 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) (92/182) of all
meta-analyses used at least one manuscripts analyzed utilized some form or combination of quantitative heterogeneity test (Figure 2).
The most widely reported statistic was I2 (41.21\%; 75/182) followed by X2 (24.18\%; 44/182). In combination, X2 and I2 (13.19\%; 24/182) were reported with greatest frequency followed by Q and I2 (12.64\%; 23/182). Authors selected a A random-effects model
more frequently than was the most common meta-analysis used
23\%(42), 25\% (46/182), next came both fixed and random-effect models
18\%(33), 21\% (39/182), fixed-effects was used in
7\%(13) 4\% (8/182) of the manuscripts, and lastly a mixed-effects model was used in
0.92\%(2) 0.55\% (1/182) of the available manuscripts (Figure 3). This leaves roughly
50\%(91) 50\% (90/182) of available manuscripts with no meta-analysis model in use to quantitatively determine heterogeneity (Figure 3).
Of the 42\%(76) which 24\% (43/182) of total studies 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) In comparison to the studies which used the random-effects model with reason to believe it is necessary, 15\% (27/182) 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) 16\% (29/182) occurrence and 0.1\%, 1\%,
20\% 10\% p-values as the least frequently used with
2\%(4), 4\%(8), 0.5\% (1/182), 0.5\% (1/182), and
2\%(4) 8\% (14/182) occurrences respectively. A forest plot was the most used heterogeneity plot with
40\%(73) 42\% (76/182) while
1\%(2) used 2\% (3/182) utilized a
forest and a L’Abbe heterogeneity plot. L’Abbé to graphically represent heterogeneity. Out of the manuscripts which created a heterogeneity plot
41\%(37) 43\% (78/182) 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), 21\% (39/182), Sensitivity analysis was second
18\%(33), 18\% (33/182), and Meta-regression was used the least
8\%(15) (Figure 4). 9\% (17/182) (Table 1). It was found that
19\%() 20\% (36/182) of the available manuscripts wrote about heterogeneity, but never actually calculated it.
57\%(104) 58\% (105/182) of manuscripts did not find significant heterogeneity,
2\%(4) 3\% (5/182) found enough evidence of heterogeneity to disregard “some” of the meta-analysis,
4\%(7) 4\% (8/182) found significant heterogeneity, and
37\%(67) 35\% (64/182) did not know whether or not there was too much heterogeneity present to perform a
meta-analysis.Summarization of evidence mapping efforts meta-analysis.
\textbf{Evidence Mapping}
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