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©The Author(s) 2025.
World J Gastrointest Surg. Apr 27, 2025; 17(4): 103696
Published online Apr 27, 2025. doi: 10.4240/wjgs.v17.i4.103696
Published online Apr 27, 2025. doi: 10.4240/wjgs.v17.i4.103696
Table 3 Comparison of predictors between the training and validation cohorts, mean ± SD
Index | Training cohort (n = 161) | Validation cohort (n = 69) | t | P value |
Age | 57.67 ± 5.80 | 57.99 ± 6.46 | -0.364 | 0.716 |
APTT | 28.16 ± 4.00 | 28.41 ± 3.99 | -0.424 | 0.672 |
AST | 38.41 ± 10.75 | 39.80 ± 12.33 | -0.860 | 0.391 |
TBIL | 21.44 ± 4.75 | 22.20 ± 5.24 | -1.081 | 0.281 |
WBC | 10.33 ± 3.59 | 9.88 ± 3.84 | 0.856 | 0.393 |
- Citation: Yuan JH, Jin YM, Xiang JY, Li SS, Zhong YX, Zhang SL, Zhao B. Machine learning-based prediction of postoperative mortality risk after abdominal surgery. World J Gastrointest Surg 2025; 17(4): 103696
- URL: https://www.wjgnet.com/1948-9366/full/v17/i4/103696.htm
- DOI: https://dx.doi.org/10.4240/wjgs.v17.i4.103696