{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003014107,0.0002844044,0.0003033106,0.0002018113,0.0001493591,0.0002833107,0.00013664,0.00008320779,0.0001341193],"category_scores_gemma":[0.0001945544,0.0002390607,0.00009982518,0.0007638744,0.00001619169,0.0001346119,0.0002104315,0.0003762625,0.00004507123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003648425,"about_ca_system_score_gemma":0.0002969393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007955028,"about_ca_topic_score_gemma":0.00001051425,"domain_scores_codex":[0.9978781,0.00006675538,0.0002765894,0.0005100668,0.0007063069,0.0005622187],"domain_scores_gemma":[0.999245,0.00005950542,0.00004408703,0.000208423,0.0001033236,0.0003396642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001436216,0.00004339836,0.9754516,0.0003010653,0.0001019876,0.0005666301,0.006439578,0.000047948,0.00002900649,0.0000140526,0.003419826,0.01344132],"study_design_scores_gemma":[0.002886282,0.002431703,0.8799216,0.0002781563,0.0007969742,0.000239112,0.002814668,0.001850024,0.00008968401,0.00008109706,0.1075804,0.001030287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916024,0.002638343,0.0001327866,0.0004167878,0.0009563301,0.001031163,0.000228859,0.0004904771,0.002502872],"genre_scores_gemma":[0.9963295,0.0003336282,0.001346823,0.0002557395,0.0007651058,0.00004244481,0.000201496,0.00003194357,0.0006933364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1041606,"threshold_uncertainty_score":0.9748614,"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."}}