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Table 2 The 24 graph-functionality features used to describe the 208 retrieved graphical displays for meta-analysis

From: Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

Functionality features of meta-analytic plots
01 - Displays summary outcome point estimate
02 - Displays summary outcome interval estimate
03 - Displays heterogeneity summary estimates (e.g., I2, Q; also, inconsistency in network meta-analysis)
04 - Displays individual study effect-size point estimates
05 - Displays individual study effect interval estimate
06 - Displays individual study meta-analytic weight/precision/N (including contribution of comparisons in network meta-analysis)
07 - Displays individual study names or identifiers
08 - Displays more than one outcome per study
09 - Displays individual study significance dichotomously (i.e., significant vs. not)
10 - Displays individual study significance continuously (i.e., allows to assess how close a study p value was to statistical significance thresholds)
11 - Informs about the likelihood, or posterior distribution, of meta-analytic parameter values
12 - Suitable to display association of effect sizes with categorical study features
13 - Suitable to display association of effect sizes with continuous study features
14 - Suitable to display individual study or study-group features (additionally or exclusively)
15 - Suitable and informative for small-sized meta-analyses (10 studies or less)
16 - Suitable and informative for medium-sized meta-analyses (say, about 50 studies)
17 - Suitable and informative for large-sized meta-analyses (say, hundreds of studies)
18 - Suitable to assess small-study effects/publication bias and other forms of biases
19 - Suitable to assess the temporal development of meta-analytic estimates
20 - Suitable to assess an excess of between-study (or study-group) effect heterogeneity (also, inconsistencies in network meta-analysis)
21 - Suitable to assess assumptions about the distribution of estimates (e.g., normality of effects)
22 - Suitable to assess the robustness of summary effect(s)
23 - Suitable to assess the robustness of heterogeneity statistics (e.g., I2, τ2, Q)
24 - Suitable to identify influential studies (i.e., outliers, leverage points)