{"id":"W4400880966","doi":"10.1016/j.gpeds.2024.100220","title":"Leveraging machine learning to study how temperament scores predict pre-term birth status","year":2024,"lang":"en","type":"article","venue":"Global Pediatrics","topic":"Infant Development and Preterm Care","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; National Institute of Mental Health; Medical School, University of Michigan; National Institute of Environmental Health Sciences; Research Institute, Nationwide Children's Hospital; University of California, Los Angeles; National Institutes of Health; Ohio State University; Natural Resources, Energy and Science Authority of Sri Lanka; National Institute on Drug Abuse; York University; Colorado State University; Nationwide Children's Hospital; National Center for Advancing Translational Sciences; Health Resources and Services Administration; Cures Within Reach; University at Buffalo; National Science Foundation; March of Dimes Foundation; Brown University","keywords":"Temperament; Term (time); Psychology; Artificial intelligence; Random forest; Machine learning; Computer science; Developmental psychology; Personality; Social psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00387285,0.0006690459,0.0004549508,0.00135825,0.0002301455,0.001148117,0.0004712687,0.0004519954,0.001069523],"category_scores_gemma":[0.01543476,0.0001625701,0.0006152604,0.0009985061,0.0003093223,0.0004972959,0.0006454164,0.001154464,0.0003252965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003604053,"about_ca_system_score_gemma":0.000432826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002871427,"about_ca_topic_score_gemma":0.00375667,"domain_scores_codex":[0.9986235,0.0008903604,0.00006337716,0.0002415981,0.0001041704,0.00007703475],"domain_scores_gemma":[0.9918755,0.005831857,0.000947974,0.0006966816,0.0004119482,0.0002359581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000277556,0.0002788135,0.9070918,0.00005342973,0.0004832475,0.0000408273,0.0002180426,0.008735757,0.001337787,0.0003211205,0.000438172,0.08072358],"study_design_scores_gemma":[0.00002248989,0.0007038399,0.7457199,0.0001275682,0.0002340469,0.0001847229,0.0003625693,0.2467604,0.001421008,0.003524112,0.0009047178,0.00003451698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622003,0.0005313432,0.03449232,0.0003004057,0.00003500433,0.00008719257,0.0006852695,0.0001399259,0.00152818],"genre_scores_gemma":[0.9896882,0.0001240443,0.00941396,0.00003716529,0.00001443664,0.00004706654,0.0004439941,0.000008601654,0.0002226254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00387285,"threshold_uncertainty_score":0.02048188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347681475000575,"score_gpt":0.2747224175258839,"score_spread":0.2612456027758782,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}