{"id":"W2957345146","doi":"10.1109/icc.2019.8761985","title":"Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques","year":2019,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Gestation; Fetus; Gestational age; Obstetrics; Birth weight; Pregnancy; Medicine; Intervention (counseling); Estimation; Low birth weight; Infant mortality; Fetal weight; Computer science; Population; Engineering; Environmental health; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007436002,0.0001832634,0.0003092282,0.0001413824,0.0004749354,0.00001053974,0.0001852723,0.0002921754,0.002674149],"category_scores_gemma":[0.0004761613,0.0001494192,0.0000445387,0.0003537367,0.00005046341,0.000425224,0.0001314401,0.001333825,0.00246754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002477223,"about_ca_system_score_gemma":0.0001804598,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03537268,"about_ca_topic_score_gemma":0.0119764,"domain_scores_codex":[0.9973882,0.0006833872,0.0007564561,0.0003567624,0.0002572334,0.000557941],"domain_scores_gemma":[0.998459,0.0006972656,0.0003020923,0.0003310101,0.0001450622,0.00006558176],"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":[0.00003906104,0.00003259902,0.9620738,0.0002600859,0.00000508666,0.00000267377,0.00440522,0.0003112435,0.0001756375,0.01355251,0.0001502697,0.0189918],"study_design_scores_gemma":[0.001430711,0.001953349,0.2117703,0.006349104,0.00005408601,0.000008909683,0.02589665,0.4330832,0.05312193,0.1555482,0.1085793,0.002204228],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648992,0.0004062174,0.009067345,0.001458662,0.0007665227,0.002102739,0.00001810864,0.0007732184,0.02050794],"genre_scores_gemma":[0.9694876,0.0004409692,0.01865526,0.0003455,0.0001066834,0.0001955811,0.00003514535,0.00003995766,0.01069326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7503034,"threshold_uncertainty_score":0.9983091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05787475321213514,"score_gpt":0.4161963035217812,"score_spread":0.358321550309646,"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."}}