## 2 Results
### Results of Synthesis  
Biomarkers reported in three or more studies providing sample sizes were selected for inclusion in the meta-analysis, resulting in three eligible biomarkers: IL-1\(\beta\), TNF-\(\alpha\), and CGRP.  
All quantitative syntheses were conducted in Python 3.10 (PyCharm IDE) with statsmodels (0.14.5), SciPy (1.16.0), NumPy (2.3.1), Pandas (2.3.1), and Plotly (6.2.0). For each experiment, we calculated Hedges' g--the standardised mean difference adjusted for small-sample bias--and its sampling variance. We prespecified an inverse-variance fixed-effect meta-analysis in which each study was weighted by the reciprocal of its variance; the pooled effect size, its standard error and 95% confidence interval were derived from these weights. Statistical heterogeneity was assessed with Cochran's Q and summarised as I\({}^{2}\). Small-study effects were investigated by visually inspecting funnel plots of effect size versus standard error and by [PERSON]'s regression test, which regresses the standardised effect on study precision and interprets a non-zero intercept as evidence of asymmetry.  
The meta-analysis using Hedges' g revealed a significant effect of BoNT on IL-1\(\beta\) levels (Figure 3), with a pooled effect size of \(-1.43\) (95% confidence interval (CI): \([-1.99,\,-0.87]\), \(p<0.00001\)). However, large heterogeneity was observed ([PERSON]'s Q = 74.29, \(p<0.000001\); I\({}^{2}\) = 90.58%) and significant publication bias was detected ([PERSON]'s test intercept = \(-5.07\), \(p=0.00368\)).  
After excluding the study contributing to high heterogeneity, the effect size decreased to \(-1.1\) (95% CI: [\(-1.67\), \(-0.54\)], \(p<0.001\)). While heterogeneity remained significant ([PERSON]'s Q = 11.63, \(p\) = 0.04030), it was reduced to a moderate level (I\({}^{2}\) = 56.99%).  
There was no individual-level data available to assess the impact of the timing of measurements on effect size. However, visual inspection suggests a trend toward a reduced magnitude of effect when IL-1\(\upbeta\) levels were measured at later time points. Similarly, no clear correlation between effect size and dose was evident based on the scatterplot analysis.  
The meta-analysis revealed a significant effect of BoNT on TNF-\(\upalpha\) levels (Figure 4), with a pooled effect size of \(-1.69\) (95% CI: [\(-2.28\), \(-1.10\)], \(p<0.000001\)) with non-significant heterogeneity observed at moderate level ([PERSON]'s Q = 10.55, \(p\) = 0.06112; I\({}^{2}\) = 52.60%) and non-significant publication bias ([PERSON]'s test intercept = \(-2.35\), \(p\) = 0.15536).  
Figure 3: Meta-analysis IL-1\(\upbeta\)[13; 14; 16; 17; 18]. **(a)** Forest plot of effect sizes (Hedges’ g) on IL-1\(\upbeta\) with 95% CI. **(b)** IL-1\(\upbeta\) results funnel plot (95% CI) with [PERSON]’s test for publication bias. **(c)** Forest plot of effect sizes (Hedges’ g) on IL-1\(\upbeta\) with 95% CI (excluding study with relatively high heterogeneity [13]). KEY: red—small effect size threshold (0.2); yellow—moderate effect size threshold (0.5); green—large effect size threshold; blue—combine effect size.  
Similarly, no clear correlation between effect size and dose or measurement time was evident based on the scatterplot analysis.  
The CGRP analysis was excluded entirely due to significant and substantial heterogeneity ([PERSON]'s Q = 4.76, \(p<0.000001\); I\({}^{2}\) = 91.06%) and the presence of significant publication bias ([PERSON]'s test intercept = \(-6.05\), \(p<0.0001\)).