{"id":"W2131992341","doi":"10.1109/cbms.2005.84","title":"Predicting Preterm Birth Using Artificial Neural Networks","year":2005,"lang":"en","type":"article","venue":"","topic":"Preterm Birth and Chorioamnionitis","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Artificial neural network; Artificial intelligence; A priori and a posteriori; Medicine; Computer science; Gestation; Machine learning; Training set; Birth weight; Set (abstract data type); Statistics; Pregnancy; Pediatrics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00009135085,0.0001356192,0.0001847815,0.00006285493,0.0001183649,0.00004040611,0.00006428672,0.00009709338,0.0005186219],"category_scores_gemma":[0.00003600939,0.0001159377,0.00009089697,0.0001231353,0.00004476062,0.0001586632,0.00003980004,0.0002287779,0.0000171696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004955377,"about_ca_system_score_gemma":0.00003511717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003594476,"about_ca_topic_score_gemma":0.00004469061,"domain_scores_codex":[0.9989305,0.00002278994,0.0002668559,0.0002215743,0.0001830626,0.0003751742],"domain_scores_gemma":[0.999451,0.00002770683,0.00005616439,0.0002149828,0.0000445172,0.0002056587],"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":[0.0007190667,0.0002179105,0.5807766,0.0001062507,0.0001379099,0.0001825889,0.000527894,0.01065433,0.007489511,0.001153767,0.0008479655,0.3971862],"study_design_scores_gemma":[0.0005752561,0.0001567706,0.01247375,0.00004734001,0.00005359046,0.000286547,0.00005540449,0.9829,0.001952848,0.00003923925,0.001301692,0.0001575623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850996,0.0001974764,0.004674753,0.0004531538,0.0003290459,0.0002187549,0.000003659623,0.0002357794,0.008787786],"genre_scores_gemma":[0.993853,0.00001354219,0.002207177,0.001038951,0.002439908,0.0000048787,0.00001144558,0.00002350961,0.0004076349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9722457,"threshold_uncertainty_score":0.5678546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02610259627198896,"score_gpt":0.2716756217062763,"score_spread":0.2455730254342874,"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."}}