In a meta-analysis manuscript, the author(s) should delineate the statistical procedures applied to aggregate effects across the included studies and should expound on the model utilized to estimate the heterogeneity of the effect sizes (e.g., random- or fixed-effect model). Since the effect sizes in the analyzed literature could be reported in various forms (e.g., r family or d family), the authors of the meta-analysis could better utilize the metric most frequently reported in the analyzed research. The following might be of interest.

Barili, F., Parolari, A., Kappetein, P. A., & Freemantle, N. (2018). Statistical primer: Heterogeneity, random- or fixed-effects model analyses? Interactive CardioVascular and Thoracic Surgery, 27(3), 317–321. https://doi.org/10.1093/icvts/ivy163

Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press. https://doi.org/10.1017/CBO9780511761676

Higgins, J. P. T., Thompson, S. G., & Spiegelhalter, D. J. (2009). A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society), 172(1), 137–159. https://doi.org/10.1111/j.1467-985X.2008.00552.x

Lin, L., & Chu, H. (2018). Quantifying publication bias in meta-analysis. Biometrics, 74(3), 785–794. https://doi.org/10.1111/biom.12817

Linden, A. H., & Hönekopp, J. (2021). Heterogeneity of research results: A new perspective from which to assess and promote progress in psychological science. Perspectives on Psychological Science, 16(2), 358–376. https://doi.org/10.1177/1745691620964193

Page, M. J., Moher, D., & McKenzie, J. E. (2021). Introduction to PRISMA 2020 and implications for research synthesis methodologists. Research Synthesis Methods, jrsm.1535. https://doi.org/10.1002/jrsm.1535

Rice, K., Higgins, J. P. T., & Lumley, T. (2018). A re-evaluation of fixed effect(s) meta-analysis. Journal of the Royal Statistical Society: Series A (Statistics in Society), 181(1), 205–227. https://doi.org/10.1111/rssa.12275

Good luck,

These links maybe useful

https://www.researchgate.net/publication/304975409_Interpreting_forest_plots_and_funnel_plots_in_meta-analysis

https://www.researchgate.net/publication/281106023_How_to_read_a_forest_plot_in_a_meta-analysis

https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/forest.html

HI, difficult to tell without seeing the paper, but if they are reporting 92.5% CI then they are maybe testing the robustness of their findings using the 'liberal' approach which suggests a range of 92.5%-97.5%. See Bradley, J.V. (1978) Robustness? British Journal of Mathematical and Statistical Psychology, 31, 144-152. just check this is not what the authors are reporting. worth checking.