{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Brown MM"],"funding":["Nova Scotia Health Research Foundation","New Brunswick Innovation Foundation","IWK Health Centre"],"pagination":["33212"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12475498"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Prediction of small (SGA) and large for gestational age (LGA) using routinely collected antenatal data remains suboptimal, particularly among nulliparous women. In this study, models for SGA (< 10<sup>th</sup> percentile) and LGA (> 90<sup>th</sup> percentile) were developed by combining grandmaternal pregnancy-related information and maternal birth characteristics (\"G0 predictors\") with maternal clinical factors available at 26 weeks' gestation (\"G1 predictors\"). The study used a cohort of first-born, singleton births to nulliparous women in Nova Scotia, Canada (1981-2011), and their mothers, from the Nova Scotia Atlee Perinatal Database. Models using G0 predictors, G1 predictors, and their combination were developed with Super Learner, an ensemble machine learning algorithm, and internal"],"journal":["Scientific reports"],"pubmed_title":["Development and validation of super learner models to predict small and large for gestational age in the second generation."],"pmcid":["PMC12475498"],"funding_grant_id":["TRF-0000000145","22523","PSO-SS-2017-1358"],"pubmed_authors":["Woolcott CG","Smith B","Brown MM","Allen VM","Kuhle S","Payne J"],"additional_accession":[]},"is_claimable":false,"name":"Development and validation of super learner models to predict small and large for gestational age in the second generation.","description":"Prediction of small (SGA) and large for gestational age (LGA) using routinely collected antenatal data remains suboptimal, particularly among nulliparous women. In this study, models for SGA (< 10<sup>th</sup> percentile) and LGA (> 90<sup>th</sup> percentile) were developed by combining grandmaternal pregnancy-related information and maternal birth characteristics (\"G0 predictors\") with maternal clinical factors available at 26 weeks' gestation (\"G1 predictors\"). The study used a cohort of first-born, singleton births to nulliparous women in Nova Scotia, Canada (1981-2011), and their mothers, from the Nova Scotia Atlee Perinatal Database. Models using G0 predictors, G1 predictors, and their combination were developed with Super Learner, an ensemble machine learning algorithm, and internal","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-03T23:23:15.046Z","creation":"2026-05-03T03:10:45.045Z"},"accession":"S-EPMC12475498","cross_references":{"pubmed":["41006777"],"doi":["10.1038/s41598-025-18466-0"]}}