{"id":"W3177144647","doi":"10.1371/journal.pone.0252025","title":"Prediction of preterm birth in nulliparous women using logistic regression and machine learning","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Preterm Birth and Chorioamnionitis","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact; McMaster University","funders":"Canadian Institutes of Health Research; McMaster University","keywords":"Logistic regression; Medicine; Confidence interval; Odds ratio; Obstetrics; Receiver operating characteristic; Pregnancy; Gestation; Population; Area under the curve; Gestational age; Gynecology; Internal medicine; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001086228,0.00008524847,0.0002750329,0.00008711949,0.00003637117,0.00000840964,0.00002399008,0.00006944156,0.00011666],"category_scores_gemma":[0.0003023623,0.00007765093,0.00001798141,0.0001418164,0.00004486589,0.00005607289,0.00004409369,0.0002192168,0.000001265202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006906471,"about_ca_system_score_gemma":0.00005498338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003317031,"about_ca_topic_score_gemma":0.00000744478,"domain_scores_codex":[0.9991717,0.00006319242,0.0002082738,0.0001800155,0.0002023813,0.0001744907],"domain_scores_gemma":[0.9995927,0.00004131324,0.00007671985,0.0001347168,0.00006129646,0.00009324465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002578452,0.0004264544,0.7793714,0.000985574,0.00007299908,0.0001477976,0.001019995,0.0000112218,0.2150339,0.00000917601,0.000002050797,0.002661503],"study_design_scores_gemma":[0.00978676,0.002527979,0.631043,0.01052996,0.0004409554,0.0004473353,0.001006132,0.09322777,0.2494892,0.0007223773,0.0003044834,0.00047401],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979273,0.001372953,0.00005559187,0.00005607539,0.00002585411,0.0001296816,0.00002651706,0.00003272055,0.0003732942],"genre_scores_gemma":[0.9974,0.0008051369,0.001151109,0.00003443013,0.00006205069,0.00000897985,0.00003548511,0.0000134982,0.0004892895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1483284,"threshold_uncertainty_score":0.3166513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09299883362524157,"score_gpt":0.2581341690975172,"score_spread":0.1651353354722757,"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."}}