Interpreting the clinical utility and generalizability of a multitask perioperative prediction model

npj Digital Medicine · Published 2026-08-03 · DOI 10.1038/s41746-026-02813-0

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Authors (2)

Xiao-Min Wo, Guo-Ming Zhang

Abstract

Abstract This Matters Arising comments on Yoon et al.’s multitask gradient boosting model for predicting acute kidney injury, postoperative respiratory failure and in-hospital mortality after non-cardiac surgery. We ask for clarification on the clinical decision context and threshold ranges in the decision curve analyses, the absolute impact of the model for rare outcomes, and calibration and potential recalibration in external cohorts to support implementation.

Abstract from DOAJ. Public domain (CC0 1.0).

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Publication details

Year
2026

Citation

Wo, X., Zhang, G. (2026). Interpreting the clinical utility and generalizability of a multitask perioperative prediction model. npj Digital Medicine. https://doi.org/10.1038/s41746-026-02813-0

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