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©The Author(s) 2024.
World J Clin Cases. Aug 26, 2024; 12(24): 5513-5522
Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5513
Published online Aug 26, 2024. doi: 10.12998/wjcc.v12.i24.5513
Table 4 Multivariate logistic regression analysis
Risk factor | β | SE | Wald χ2 | P value | OR | 95%CI |
Age | 0.056 | 0.017 | 11.434 | 0.001 | 1.058 | 1.024-1.093 |
Basal body temperature | −1.211 | 0.365 | 11.157 | 0.001 | 0.286 | 0.231-0.976 |
Operating room temperature | −0.066 | 0.041 | 2.688 | 0.101 | 0.936 | 0.864-1.013 |
Duration of anesthesia | −0.008 | 0.003 | 8.458 | 0.004 | 0.992 | 0.986-0.997 |
Operation duration | 0.011 | 0.003 | 12.787 | < 0.001 | 1.011 | 1.005-1.018 |
- Citation: Zhu K, Zhang ZX, Zhang M. Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients. World J Clin Cases 2024; 12(24): 5513-5522
- URL: https://www.wjgnet.com/2307-8960/full/v12/i24/5513.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v12.i24.5513