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. 2022 Nov 9;9:1004205. doi: 10.3389/fsurg.2022.1004205

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© 2022 Chen, Zhang, Bai, Liu, Liang and He.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Predictor selection using LASSO regression analysis with 10-fold cross-validation. (A) Tuning parameter (lambda) selection of deviance in the LASSO regression based on the minimum criteria (left dotted line) and the 1-SE criteria (right dotted line). (B) A coefficient profile plot was created against the log (lambda) sequence. In the present study, predictors were selected according to the 1-SE criteria (right dotted line), where four non-zero coefficients were selected. LASSO, least absolute shrinkage and selection operator; SE, standard error.