Camila Isabelle Silva Martins, Bianca Arruda Manchester de Queiroga, Angélica Galindo Carneiro Rosal, Ana Augusta de Andrade Cordeiro
Comparative analysis of cognitive-linguistic evaluation in normal hearing schoolchildren and users of cochlear implants
CoDAS
conversion temporary permanent stoma ps following rectal cancer surgery significantly impacts quality life patients however currently lack practical preoperative tools predict ps formation purpose study establish preoperative predictive model ps using machine learning algorithms guide clinical practice retrospective study analyzed clinical data total 655 patients underwent anterior resection rectal cancer 552 patients one medical center 103 another machine learning algorithms five predictive models developed thoroughly evaluated predictive performance model superior predictive accuracy underwent additional validation using independent testing cohort external validation cohort shapley additive explanations shap approach employed elucidate predictive factors influencing model providing indepth visual analysis decisionmaking process eight variables selected construction model support vector machine svm model exhibited superior predictive performance training set evidenced auc 0854 95 ci08030904 performance corroborated testing set external validation set model demonstrated auc 0851 95ci07480954 0815 95ci07100919 respectively indicating efficacy identifying ps modelhttpsyangsu2023shinyappsiopsrisk indicated robust predictive performance identifying ps anterior resection rectal cancer potentially guiding surgeons preoperative stratification patients thus informing individualized treatment plans improving patient outcomes