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For instance, Zhao et al [23] constructed machine learning models to predict the p CR to NAC based on clinicopathological variables. Similarly, Zhang and coworkers [24-30] developed machine learning models that incorporated clinicopathological features, radiomic features, and pathomic features to forecast the p CR following NAC. Additionally, Sammut et al [31] and Chen et al [32] created models using multi-omics data.
J Med Internet Res 2025;27:e69864
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Citation Accuracy Challenges Posed by Large Language Models
JMIR Med Educ 2025;11:e72998
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Efficacy of eHealth Interventions for Hemodialysis Patients: Systematic Review and Meta-Analysis
When the study by Zhao et al [45] was excluded, a significant reduction in the overall effect size was observed, with I² decreasing from 93.87% to 79.9%. This suggests that a substantial impact was made on the overall result of this study, which may have been a significant source of the detected heterogeneity. The potential overestimation of the intervention effect in the study of Zhao et al [45] may be attributed to the lack of rigorous randomization and blinding procedures.
J Med Internet Res 2025;27:e67246
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