{"id":"W4389283805","doi":"10.2196/52059","title":"Machine Learning Models for Prediction of Maternal Hemorrhage and Transfusion: Model Development Study","year":2023,"lang":"en","type":"article","venue":"JMIR Bioinformatics and Biotechnology","topic":"Maternal and fetal healthcare","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Medicine; Receiver operating characteristic; Logistic regression; Blood transfusion; Nomogram; Obstetrics; Area under the curve; Predictive modelling; Machine learning; Artificial intelligence; Computer science; Surgery; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01031876,0.001536379,0.001215316,0.001331157,0.0003615252,0.0009856077,0.001194573,0.0009927464,0.001296921],"category_scores_gemma":[0.0172761,0.0004887664,0.001662988,0.0009446087,0.0002442114,0.0008071159,0.0008112805,0.001670889,0.0003648201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001410929,"about_ca_system_score_gemma":0.002166131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02106092,"about_ca_topic_score_gemma":0.00931153,"domain_scores_codex":[0.997972,0.001530689,0.00007106613,0.0001747892,0.0001367443,0.0001148994],"domain_scores_gemma":[0.9801339,0.01725234,0.0005760157,0.0003612758,0.001478792,0.000197723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008615117,0.0008993616,0.0712327,0.0002411511,0.0009409197,0.0001558268,0.0001305215,0.8681351,0.0002611689,0.001113233,0.002984903,0.05304355],"study_design_scores_gemma":[0.00003967533,0.0001832172,0.002684045,0.0000332592,0.00008401775,0.00001880407,0.00001571957,0.9962554,0.0001045719,0.0003893746,0.0001839927,0.000007781277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9046386,0.005539371,0.08258878,0.001774441,0.0001585283,0.0004687099,0.0015209,0.0006635957,0.002647056],"genre_scores_gemma":[0.9680857,0.001296271,0.02761535,0.0001594186,0.0000689099,0.0003873007,0.001312909,0.00003842312,0.00103564],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02106092,"threshold_uncertainty_score":0.05457151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03786617389960379,"score_gpt":0.2787201086145447,"score_spread":0.2408539347149409,"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."}}