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Predicting Continence Post-RARP by nashenweiss is a document available to read on EtoBox.

What is Predicting Continence Post-RARP about?

The document describes a study that used a deep learning model to predict urinary continence recovery after robot-assisted radical prostatectomy (RARP) based on automated performance metrics (APMs) and clinical data. The model was able to predict continence with a concordance index of 0.6 and mean absolute error of 85.9 days. APMs were ranked higher than clinical features by the model. Surgeons with more efficient APMs had higher continence rates at three and six months in a historical patient cohort.

Author

nashenweiss

Language

EN