{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003183391,0.0005390245,0.00042377,0.001092489,0.000215087,0.0005991794,0.0005448067,0.0004452679,0.0006822694],"category_scores_gemma":[0.01609447,0.0003168547,0.0005998616,0.0008512784,0.0002557632,0.0004386688,0.0005041838,0.0006195595,0.0002425999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006529964,"about_ca_system_score_gemma":0.0008208925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01791014,"about_ca_topic_score_gemma":0.01293657,"domain_scores_codex":[0.9989727,0.0005858171,0.00008239601,0.0001270129,0.0001442645,0.00008785618],"domain_scores_gemma":[0.995171,0.00230171,0.001641853,0.0002135585,0.0004259803,0.0002457939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001370608,0.00001712816,0.9975495,0.00000893803,0.00003871803,0.00003316877,0.00001868593,0.0003144305,0.00005048011,0.000007712725,0.00005906937,0.00176513],"study_design_scores_gemma":[0.00004096502,0.0002256643,0.9784944,0.00004903113,0.00007507579,0.0003634769,0.0001660321,0.02004345,0.000168998,0.0001186769,0.0002435889,0.00001070826],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972097,0.0005853293,0.001163142,0.0002181719,0.000008019145,0.00002220869,0.000451466,0.00001396382,0.0003280534],"genre_scores_gemma":[0.9982898,0.0002302898,0.0008650813,0.00001516194,0.000008399249,0.00001516937,0.0004744657,0.000002557825,0.00009917052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01791014,"threshold_uncertainty_score":0.03561175,"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."}}