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

ABSTRACT: To assess the methodological quality and the risk of bias, of studies that developed prediction models using Machine Learning (ML) techniques to estimate prenatal birthweight. We conducted a systematic review, searching the PubMed databases between 01/01/2018 and 01/08/2022, for studies that developed fetal weight prediction models using ML. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias. We measured the overall adherence to the TRIPOD reporting checklist, provided a detailed analysis of the methodological quality of each study, and examined risk of bias in specific

SUBMITTER: Gao J 

PROVIDER: S-EPMC12225210 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature