ArticleWorld journal of nephrology2026
Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?
Article in World journal of nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Both traditional statistics, such as the logistic regression (LR) model, and machine learning (ML) have strengths and limitations for predicting outcomes after kidney transplantation. The LR model is simple, interpretable, and reliable with small datasets. ML can capture complex, nonlinear patterns and manage many variables, but it needs larger, high-quality datasets to reach its full potential. In the recent issue of
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