## 3 Results
### Meta-Analysis
#### 3.7.1 Descriptive Analysis
Effects Modelling  
The mixed-effects regression model: funnel plot asymmetry. A regression test for funnel plot asymmetry was conducted for the mixed-effects regression model to test the potential of publication bias or other small-study effects in the meta-analysis. The test was conducted at a 5% (0.05) level of significance, testing the following hypotheses:  
\(H_{0}\): \(\beta=0\) (there is no asymmetry in the funnel plot, i.e., the regression coefficient of the predictor is equal to zero). \(H_{1}\): \(\beta\
eq 0\) (there is an asymmetry in the funnel plot, i.e., the regression coefficient of the predictor is not equal to zero). The test yielded z = 0.0117, \(p\) = 0.9906, limit estimate (as sei\(\rightarrow\)0): b = 0.7247 (95% CI: \(-\)9.4763, 10.9256). A \(p\)-value of 0.9906 is much larger than 0.05; we failed to reject the null hypothesis, concluding that there is no significant evidence of asymmetry in the funnel plot. This suggests no indication of publication bias or small-study effects at the 5% significance level. The limit estimate (as sei\(\rightarrow\)0) was b = 0.7247, and the confidence interval, (95% CI: \(-\)9.4763, 10.9256) supports the existence of significant uncertainty that may be due to heterogeneity or variation across reviewed studies. However, the wide CI raises concerns that may need further investigation. These findings are presented in Figure 8, where most of the standard errors were equal to zero or close to zero. The funnel plot, which plotted the standard error (precision) against the observed F1-scores, showed that all points were centered on or near the vertical line at zero. This symmetry further supports the conclusion of minimal evidence for publication bias, with observed outcomes appearing consistent across studies, regardless of their precision or sample size variability. Figure 7: F1-scores—sample sizes scatter plot. The meta-analysis enabled the common effects and the random effects models to be conducted, see Table 6, yielding a pooled effect size of 0.7897 (95% CI: 0.7883, 0.7910) under the common effects model and 0.7889 (95% CI: 0.7511, 0.8268) under the random effects model. Both models supported a common effect size of 0.79, supported by a \(p\)-value of zero in both cases, indicating that the effect size was statistically significant. Standardized Mean Differences  
An \(\mathrm{I}^{2}\) statistic of 99.8% in Table 7 indicates an extremely high heterogeneity, suggesting large variations between studies with a limited contribution from the random sampling error. This is further supported by the tau-squared (\(\tau^{2}\)) estimate of 0.0254 (95% CI: 0.0187, 0.0376), showing significant variance across studies. The heterogeneity test, conducted at a 5% significance level, produced a Q statistic of 29,682.28 (df = 68, \(p\)-value = 0), confirming that the observed variability is less likely to be due to chance. A forest plot was constructed to summarize the meta-analysis data. The forest plot shows that the AI/ML approaches demonstrated reliable performance, as confirmed by most F1-scores that were close to 0.8 and restricted confidence intervals of widths ranging from 0.02 to 0.06 being the widest. Most studies had a width of 0.02; a few studies, for instance [55], had a wider confidence interval of 0.06 with corresponding lower F1-scores, suggesting poor performance of AI/ML approaches. The overall F1-score across studies is much higher than the baseline, assuming a threshold of 0.5, as proven by the pooled \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SMD** & **95\% CI** & **z** & \(p\)**-Value** \\ \hline Common effects model & 0.7897 & [0.7883, 0.7910] & 1126.07 & 0 \\ \hline Random effects model & 0.7889 & [0.7511, 0.8268] & 40.82 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Common and random effects model results.