In meta-analysis, heterogeneity in statistical measures across primary studies can significantly affect the efficiency of data synthesis and the accuracy of result interpretation. Such inconsistencies may introduce bias in effect size estimation and increase the complexity of pooled analyses. Therefore, establishing standardized approaches for data type transformation and harmonizing different statistical measures has become a critical step in ensuring the quality of meta-analyses. To achieve efficient and scientifically rigorous data integration, researchers need to master systematic data transformation techniques and develop standardized processing strategies. Based on this need, this study provides a comprehensive summary of effect size transformation methods in meta-analysis, focusing on standardizing binary and continuous variables. It offers practical guidance to support researchers in applying these methods consistently and accurately.
The overlap of literature in umbrella reviews can affect the reliability and accuracy of research conclusions, leading to results with a higher risk of bias. Therefore, it becomes crucial to assess the degree of overlapping and how to handle it. In order to avoid redundant calculations and reduce the risk of bias, researchers need to quantify the degree of literature overlap and adopt corresponding processing strategies. This paper provides a detailed introduction to the calculation methods of overlapping and different strategies for handling overlapping, aiming to provide a reference and guidance for domestic scholars' understanding and application of this method.
Neuroblastoma (NB) is the most common extracranial solid malignant tumor in children. NB has various clinical manifestations, many of which are not specific, which ultimately lead to the delayed diagnosis of the tumor. In order to provide guidance for the identification of paediatric NB, the guideline for the identification and referral of suspected paediatric neuroblastoma is formulated and complied using a standard formulation process, and has received input from multidisciplinary experts, based on existing evidence, clinical practices and China's national conditions.
