{"id":"W3022078172","doi":"10.2196/15411","title":"Prediction of Preeclampsia and Intrauterine Growth Restriction: Development of Machine Learning Models on a Prospective Cohort","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Prospective cohort study; Predictive modelling; Preeclampsia; Intrauterine growth restriction; Medicine; Pregnancy; Internal medicine; Gestation","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":[0.01308568,0.000908125,0.000871242,0.00246976,0.000428934,0.001008205,0.001100474,0.0007706287,0.001478536],"category_scores_gemma":[0.01621811,0.0003918223,0.001879991,0.001004223,0.0002696812,0.0006670735,0.0009650445,0.001952798,0.0004146984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007999124,"about_ca_system_score_gemma":0.001066983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007325105,"about_ca_topic_score_gemma":0.004700813,"domain_scores_codex":[0.9982729,0.0008377987,0.0001663252,0.0004278753,0.000185902,0.0001091297],"domain_scores_gemma":[0.9854143,0.01140075,0.0008332233,0.0009607514,0.001001365,0.0003896388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001128379,0.000789516,0.6864632,0.00009476415,0.001040332,0.0004445848,0.0001790873,0.243863,0.0006534039,0.0004868088,0.004366663,0.06049037],"study_design_scores_gemma":[0.00005799091,0.0003639982,0.05267573,0.00005146439,0.000239987,0.0001584263,0.00008196097,0.9443229,0.0005808988,0.0008300202,0.0006071032,0.0000295589],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9432212,0.0005114676,0.05046092,0.0006188145,0.00008647239,0.0002521063,0.003872332,0.0003030849,0.000673669],"genre_scores_gemma":[0.9559687,0.0002477934,0.03613947,0.00007777136,0.00005380042,0.0002916892,0.006778832,0.00003878704,0.0004032254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01308568,"threshold_uncertainty_score":0.06920451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536793263062729,"score_gpt":0.2616337559565901,"score_spread":0.2262658233259628,"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."}}