Methodological conduct and risk of bias in studies on prenatal birthweight prediction models using machine learning techniques: a systematic review

Abstract

Methodological quality of the ML-based prediction models for prenatal birthweight estimation was generally poor, with most studies at high risk of bias. There is an urgent need for improvements in the design and reporting of these studies. The adaptation of the TRIPOD and PROBAST statements specifically for ML models should be promoted to enhance transparency and reproducibility, which would facilitate the wider clinical application of ML-based prediction models and reduce research waste.

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