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A total of 3 independent variables were filtered by LASSO analysis from the 22 candidate factors. The AUC of the training and validation sets were 0.833 (95%CI 0.774-0.894) and 0.817 (95%CI 0.711-0.922), respectively, which indicated a good discrimination ability. The calibration charts showed that the prediction probability and the actual probability fitted well. The DCA of the prediction model indicated an excellent clinical efficacy. The proposed nomogram can quantitatively and conveniently predict the recurrence rate of CSDH after bu
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