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Cited by in F6Publishing

For:	Rahman SA, Maynard N, Trudgill N, Crosby T, Park M, Wahedally H, Underwood TJ, Cromwell DA; NOGCA Project Team and AUGIS. Prediction of long-term survival after gastrectomy using random survival forests. Br J Surg 2021:znab237. [PMID: 34297818 DOI: 10.1093/bjs/znab237] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]

For:

Rahman SA, Maynard N, Trudgill N, Crosby T, Park M, Wahedally H, Underwood TJ, Cromwell DA; NOGCA Project Team and AUGIS. Prediction of long-term survival after gastrectomy using random survival forests. Br J Surg 2021:znab237. [PMID: 34297818 DOI: 10.1093/bjs/znab237] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]

Number	Citing Articles
1	Talebi A, Celis-morales CA, Borumandnia N, Abbasi S, Pourhoseingholi MA, Akbari A, Yousefi J. Predicting metastasis in Gastric cancer patients: machine learning-based approaches.. [DOI: 10.21203/rs.3.rs-2285542/v1] [Reference Citation Analysis]
2	Yang L, Fan X, Qin W, Xu Y, Zou B, Fan B, Wang S, Dong T, Wang L. A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer. Cancer Med 2022. [PMID: 35491970 DOI: 10.1002/cam4.4782] [Reference Citation Analysis]
3	Ko Y, Shin H, Shin J, Hur H, Huh J, Park T, Kim KW, Lee I. Artificial Intelligence Mortality Prediction Model for Gastric Cancer Surgery Based on Body Morphometry, Nutritional, and Surgical Information: Feasibility Study. Applied Sciences 2022;12:3873. [DOI: 10.3390/app12083873] [Reference Citation Analysis]